高维遥感数据土地覆盖特征提取与分类研究
|
摘要
高维遥感数据的分类与模式识别与常规的多波段遥感数据的分类具有显著的区别。受波段维数增加的影响，为对分类中需要使用的统计参数估计进行比较精确的，训练样本数要远高于常规多波段遥感数据的分类中所使用的样本数。Hughes现象表明，高维遥感数据分类前的特征提取与处理，是一项必要的、有效的工作。因而，采取何种有效的手段进行高维遥感数据分类前的特征提取，也是当前遥感分类与识别领域一个非常值得重视的研究方向。
本文对利用高维遥感数据进行土地覆盖分类过程中的图象特征表达、分类数据的预处理、特征提取、空间维信息表达与处理、分类算法等进行了综合的分析，并针对其中若干关键问题进行了具体的研究与专题论述。最后对将特征提取技术应用于实际工作，进行了基于NOAA-AVHRR时间序列数据进行中国土地覆盖分类研究。
主要研究内容与创新点包括：
(1)、改进并提出了基于频域维纳滤波器方法的空域图象模糊复原算法。
在针对高分辨率图象中点扩散函数(PSF)概念分析的基础上，分析和讨论了基于CBERS-1图象的点扩散函数估计与图像复原算法。能对不同大气条件下的CBERS-1图象进行点扩散函数估计，采用维纳滤波器方法进行与反卷积算子计算，复原后的图象比原始图象清晰度有显著提高，该方法简单、数据运算量小。
(2)、提出了基于均值／标准差比的差值序列进行排序的SRROD滤波器。
通过对窗口内像元值进行排序，计算排序后的序列中子序列的均值和标准差，求其连续子序列的标准差与均值的比值并求归一化差值。通过将差值与给定的阈值比较可以有效地检测出脉冲噪声；检测出的噪声点的像元值取窗口内像元值的排序均值。该算法在模拟图象和真实的SeaWiFS噪声图象中的试验表明其具有优于其他算法的处理效果。并对非递归实现中的盲目参数优化进行了初步讨论。
(3)、发展了基于遗传算法的高维遥感图象特征选择技术。
比较全面地总结了当前模式识别与分类中的主要特征选择技术。在此基础上，发展了基于遗传算法的高维遥感图象特征选择技术。利用遗传算法的全局最优搜索能力，可以搜索出在不同特征组合条件下的最佳波段分类组合。初步的试验结果，该方法相对于前向选择算法具有更好的特征提取效果。
(4)、发展了基于遗传算法(GA)和小波变换的特征提取方法。
比较全面地总结了当前模式识别与分类中的主要特征提取技术。在前人工作
的基础上，将GA算法的全局优化搜索能力与小波变换的多分辨率多尺度特征提
取能力结合，发展了基于GA和小波变换的特征提取方法。对16种土地覆盖类
型的识别结果表明，该方法相对于其他方法如主成分分析(PCA)、典范分析
(DAFE)、决策边界特征提取(DBFE)等具有更好的特征提取效果。
(5)、提出了金字塔型分类器解决在多分辨率下对土地覆盖类型的光谱纹理
表现的综合与分类的框架和思路。
分析了不同分辨率条件下，土地覆盖类型的空间纹理、结构、几何表现。作
者认为，混合像元的出现并不是只会造成类别的误分现象。相反，有时候一种特
定的混合地表覆盖类型在低分辨率图象上会表现出独特的光谱特征。在不同分辨
率级别上的图象特征，可以用来辨别不同级别、层次的地物类型。基于这一观点，
提出了金字塔型分类器解决在多分辨率下对土地覆盖类型的光谱纹理表现的综
合与分类的框架和思路。并结合离散小波变换(D盯)进行了初步的分类试验，
结果表明，该方法确实能在一定程度上提高分类效果。
(6)、提出了基于G^算法与反传算法(即)结合进行多层感知机分类器(MLP)
神经元网络权重初始化训练、网络结构训练的方法。
利用GA算法的全局优化搜索能力和BP算法的局部最优搜索能力，提出了
基于GA算法与MLP分类器结合进行神经元网络权重初始化训练、并利用BP
算法进行神经元网络结构反向传递训练的方法。该方法比单纯采用BP算法更能
收敛到最小总误差.并根据训练过程中网络权重结构的调整进行分析与检验。
(7)、进行了基于N0从^vHRR时间序列数据去云处理与特征提取的中国土
地扭盖分类研究。
根据NOAA AVHRR时间序列数据的特点，针对六种典型云覆盖情况，提出
了多时相NDVI去云处理方法。将去云处理后的NDVI数据集应用于基于PCA
的最大似然分类进行了中国土地覆盖分类。取2000个随机分布的样点并以1:
400万的中国植被类型图作为标准的植被类型分布进行精度检验表明，与基于原
始NDVI数据集的MLC分类比较，分类精度提高巧.62%。这证明采用多时相
NDvi去云处理方法并结合特征提取技术能有效提高数据质量，增强不同土地覆
盖类型之间的类别可分离性，从而提高总分类精度。
Classification and pattern recognition of high dimensional remote sensing data is distinctly different from traditional multi-channel remote sensing classification techniques. Regarding to the abrupt increase of spectral dimensionality, far more training samples than traditional multi-spectral classification are needed to accurately estimate those statistical variables which are used to describe pattern properties existed in feature space. Hughes phenomena shows that feature extraction and preprocessing before classification of high dimensional remote sensing data are theoretically necessary and effective in practice. Thus, the exploitation of reliable and efficient feature extraction techniques for high dimensional data has been one of the most desirable research topics in remote sensing pattern classification and recognition fields.
This dissertation investigates issues on methodology and applications of feature extraction and image classification for high dimensional remote sensing data. We analyzes and discusses the main accuracy influences existed in image feature representation, data preprocessing, feature extraction, spatial feature representation and processing, image classification algorithm in a scenario of land cover classification and recognition based on high dimensional remote sensing data. We also apply feature extraction techniques on practical issues, in which we discusses the application of NOAA-AVHRR 1km time series data for land cover classification in China.
Main research topics and initiatives in this thesis include:
1. An improved spatial domain image deblurring algorithm is proposed based on wiener filter and deconvolution.
Through analysis the concept of point spread function (PSF) for high resolution remote sensing imagery, we discuss estimation of the synthesized point spread function and image restoration algorithm. This PSF estimation algorithm could be applied on CBERS-1 satellite visible and infra-red images in different atmospheric conditions without suffering restoration performance. We use wiener filter to compute the deconvolution operator. The quality of the imagery restored from the deconvolution operator is evidentially improved. Moreover, since the restoration procedure is performed in spatial domain, it is relatively simple and low computational complexity.
2. A non-linear adaptive filter for impulse noises removal been proposed in this thesis.
The key point of the algorithm is to sort the pixel values, to compute the sequences of standard deviations and means, and then to take the normalized differences between two successive standard deviation/mean ratios. Noise detection is achieved by thresholding these differences. Noise suppression is achieved by replacing the pixel value with the rank-ordered mean. The algorithm has been tested on simulated data and real SeaWiFS image so that its superiority is established. By considering the generalized impulse noise model discussed in the paper, we analyzed the quantitative relationship between the noise removal performance and the threshold parameters. Finally, the strategy for retrieval of the optimized thresholds has also been presented for non-recursive implementation.
3. A new feature selection technique based on genetic algorithm is proposed for selection of optimal high dimensional subset data using classification training samples.
The main popular feature subset selection technologies are reviewed in this thesis. We have proposed a new feature selection technique based on genetic algorithm (GA). This algorithm could heuristically search the optimal feature subset from different classification band combinations. Preliminary maximum likelihood classification results show that the proposed genetic algorithm is more probably to find the best feature subset.
4. A new feature extraction algorithm based on GA and wavelet transform is proposed for high dimensional data reduction and classification accuracy improvement.
The main popular feature extraction technologies are reviewed in this thesis.
本文对利用高维遥感数据进行土地覆盖分类过程中的图象特征表达、分类数据的预处理、特征提取、空间维信息表达与处理、分类算法等进行了综合的分析，并针对其中若干关键问题进行了具体的研究与专题论述。最后对将特征提取技术应用于实际工作，进行了基于NOAA-AVHRR时间序列数据进行中国土地覆盖分类研究。
主要研究内容与创新点包括：
(1)、改进并提出了基于频域维纳滤波器方法的空域图象模糊复原算法。
在针对高分辨率图象中点扩散函数(PSF)概念分析的基础上，分析和讨论了基于CBERS-1图象的点扩散函数估计与图像复原算法。能对不同大气条件下的CBERS-1图象进行点扩散函数估计，采用维纳滤波器方法进行与反卷积算子计算，复原后的图象比原始图象清晰度有显著提高，该方法简单、数据运算量小。
(2)、提出了基于均值／标准差比的差值序列进行排序的SRROD滤波器。
通过对窗口内像元值进行排序，计算排序后的序列中子序列的均值和标准差，求其连续子序列的标准差与均值的比值并求归一化差值。通过将差值与给定的阈值比较可以有效地检测出脉冲噪声；检测出的噪声点的像元值取窗口内像元值的排序均值。该算法在模拟图象和真实的SeaWiFS噪声图象中的试验表明其具有优于其他算法的处理效果。并对非递归实现中的盲目参数优化进行了初步讨论。
(3)、发展了基于遗传算法的高维遥感图象特征选择技术。
比较全面地总结了当前模式识别与分类中的主要特征选择技术。在此基础上，发展了基于遗传算法的高维遥感图象特征选择技术。利用遗传算法的全局最优搜索能力，可以搜索出在不同特征组合条件下的最佳波段分类组合。初步的试验结果，该方法相对于前向选择算法具有更好的特征提取效果。
(4)、发展了基于遗传算法(GA)和小波变换的特征提取方法。
比较全面地总结了当前模式识别与分类中的主要特征提取技术。在前人工作
的基础上，将GA算法的全局优化搜索能力与小波变换的多分辨率多尺度特征提
取能力结合，发展了基于GA和小波变换的特征提取方法。对16种土地覆盖类
型的识别结果表明，该方法相对于其他方法如主成分分析(PCA)、典范分析
(DAFE)、决策边界特征提取(DBFE)等具有更好的特征提取效果。
(5)、提出了金字塔型分类器解决在多分辨率下对土地覆盖类型的光谱纹理
表现的综合与分类的框架和思路。
分析了不同分辨率条件下，土地覆盖类型的空间纹理、结构、几何表现。作
者认为，混合像元的出现并不是只会造成类别的误分现象。相反，有时候一种特
定的混合地表覆盖类型在低分辨率图象上会表现出独特的光谱特征。在不同分辨
率级别上的图象特征，可以用来辨别不同级别、层次的地物类型。基于这一观点，
提出了金字塔型分类器解决在多分辨率下对土地覆盖类型的光谱纹理表现的综
合与分类的框架和思路。并结合离散小波变换(D盯)进行了初步的分类试验，
结果表明，该方法确实能在一定程度上提高分类效果。
(6)、提出了基于G^算法与反传算法(即)结合进行多层感知机分类器(MLP)
神经元网络权重初始化训练、网络结构训练的方法。
利用GA算法的全局优化搜索能力和BP算法的局部最优搜索能力，提出了
基于GA算法与MLP分类器结合进行神经元网络权重初始化训练、并利用BP
算法进行神经元网络结构反向传递训练的方法。该方法比单纯采用BP算法更能
收敛到最小总误差.并根据训练过程中网络权重结构的调整进行分析与检验。
(7)、进行了基于N0从^vHRR时间序列数据去云处理与特征提取的中国土
地扭盖分类研究。
根据NOAA AVHRR时间序列数据的特点，针对六种典型云覆盖情况，提出
了多时相NDVI去云处理方法。将去云处理后的NDVI数据集应用于基于PCA
的最大似然分类进行了中国土地覆盖分类。取2000个随机分布的样点并以1:
400万的中国植被类型图作为标准的植被类型分布进行精度检验表明，与基于原
始NDVI数据集的MLC分类比较，分类精度提高巧.62%。这证明采用多时相
NDvi去云处理方法并结合特征提取技术能有效提高数据质量，增强不同土地覆
盖类型之间的类别可分离性，从而提高总分类精度。
Classification and pattern recognition of high dimensional remote sensing data is distinctly different from traditional multi-channel remote sensing classification techniques. Regarding to the abrupt increase of spectral dimensionality, far more training samples than traditional multi-spectral classification are needed to accurately estimate those statistical variables which are used to describe pattern properties existed in feature space. Hughes phenomena shows that feature extraction and preprocessing before classification of high dimensional remote sensing data are theoretically necessary and effective in practice. Thus, the exploitation of reliable and efficient feature extraction techniques for high dimensional data has been one of the most desirable research topics in remote sensing pattern classification and recognition fields.
This dissertation investigates issues on methodology and applications of feature extraction and image classification for high dimensional remote sensing data. We analyzes and discusses the main accuracy influences existed in image feature representation, data preprocessing, feature extraction, spatial feature representation and processing, image classification algorithm in a scenario of land cover classification and recognition based on high dimensional remote sensing data. We also apply feature extraction techniques on practical issues, in which we discusses the application of NOAA-AVHRR 1km time series data for land cover classification in China.
Main research topics and initiatives in this thesis include:
1. An improved spatial domain image deblurring algorithm is proposed based on wiener filter and deconvolution.
Through analysis the concept of point spread function (PSF) for high resolution remote sensing imagery, we discuss estimation of the synthesized point spread function and image restoration algorithm. This PSF estimation algorithm could be applied on CBERS-1 satellite visible and infra-red images in different atmospheric conditions without suffering restoration performance. We use wiener filter to compute the deconvolution operator. The quality of the imagery restored from the deconvolution operator is evidentially improved. Moreover, since the restoration procedure is performed in spatial domain, it is relatively simple and low computational complexity.
2. A non-linear adaptive filter for impulse noises removal been proposed in this thesis.
The key point of the algorithm is to sort the pixel values, to compute the sequences of standard deviations and means, and then to take the normalized differences between two successive standard deviation/mean ratios. Noise detection is achieved by thresholding these differences. Noise suppression is achieved by replacing the pixel value with the rank-ordered mean. The algorithm has been tested on simulated data and real SeaWiFS image so that its superiority is established. By considering the generalized impulse noise model discussed in the paper, we analyzed the quantitative relationship between the noise removal performance and the threshold parameters. Finally, the strategy for retrieval of the optimized thresholds has also been presented for non-recursive implementation.
3. A new feature selection technique based on genetic algorithm is proposed for selection of optimal high dimensional subset data using classification training samples.
The main popular feature subset selection technologies are reviewed in this thesis. We have proposed a new feature selection technique based on genetic algorithm (GA). This algorithm could heuristically search the optimal feature subset from different classification band combinations. Preliminary maximum likelihood classification results show that the proposed genetic algorithm is more probably to find the best feature subset.
4. A new feature extraction algorithm based on GA and wavelet transform is proposed for high dimensional data reduction and classification accuracy improvement.
The main popular feature extraction technologies are reviewed in this thesis.
引文
陈国良，王煦法，庄镇泉，王东生编，1996。遗传算法及其应用。北京：人民邮电出版社。
胡宝新、李小文、朱重光、Alan Straler。大倾角光学遥感中大气点扩散函数的近似模型。中国图像图形学报，1996，1(1)：19-29。
胡昌华、张军波、夏军、张伟，1999。基于MATLAB的系统分析与设计——小波分析。西安：西安电子科技大学出版社。
李小文、王锦地，1995。植被光学遥感模型与植被结构参数化。北京：科学出版社。
刘建贵，1999。高光谱城市地物及人工目标识别与提取。中国科学院遥感应用研究所博士论文。北京：中国科学院遥感应用研究所。
刘明亮，2001。中国土地利用/土地覆盖变化与陆地生态系统植被碳库和生产力研究。中国科学院遥感应用研究所博士论文。北京：中国科学院遥感应用研究所。
刘强，2002。地表组分温度反演方法及遥感像元的尺度结构。中国科学院遥感应用研究所博士论文。
刘正军、王长耀、延昊等，2003。基于Fuzzy ARTMAP神经网络的高分辨率图像土地覆盖分类及其评价。中国图像图形学报，8(2)：151-154。
罗迪，1999．基于AVHRR和地学空间数据的中国土地覆盖分类．中国科学院遥感应用研究所硕士论文。北京：中国科学院遥感应用研究所。
牛铮、朱重光、王长耀，1997。斜视角度下大气交叉辐射影响分析。遥感学报，1997，1(2)：88-93。
潘正均、康立山、陈毓屏。演化计算。北京：清华大学出版社，1998。
浦瑞良、宫鹏，2000。高光谱遥感及其应用。北京：高等教育出版社。
沈清、汤霖，1991。模式识别导论。长沙：国防科技大学出版社。
史培军、宫鹏等，2000。土地利用/覆盖变化研究的方法与实践。北京：科学出版社。
孙即祥、王晓华、钟山、张帆、史慧敏，2001。模式识别中的特征提取与计算机视觉不变量。北京：国防工业出版社。
杨福生，1999。小波变换的工程分析与应用。北京：科学出版社。
张继贤，李德仁，1996。影像纹理的多尺度分析。环境遥感，1996，11(1)，1-13。
张继贤，1994。影像纹理的多层次分析方法。武汉测绘科技大学博士论文。武汉：武汉测绘科技大学。
赵荣椿、赵忠明、崔更生，1996。数字图像处理导论。西安：西北工业大学出版社。
Abreu, E, Lightone M, Mitra S K et al, 1996. A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE IP,5(6):pp.1012-1025
Aha, D. W., and Bankert, R. L., 1996, A comparative evaluation of sequential feature selection algorithms. In Learning from Data: Arti. cial Intelligence and Statistics V, edited by D. Fisher and J.-H. Lenz (New York: Springer Verlag), pp. 199-206.
Almuallim, H. and Dietterich, T., 1994. Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1-2): 279-305.
Anys, H. et al, 1994. Texture analysis for the mapping of urban areas using airborne MEIS-Ⅱ images, the first International airborne remote sensing conference and exhibition, Strasbourg, France, 11-15 Sept. 1994.
Atkinson, P. M. and Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers and Geosciences, 26: pp.361-371.
Bandyopadhyay,S.,Pal S.K.,2001． Pixel classification using variable string genetic algorithms with chromosome differentiation.IEEE Trans.Geoscience & Remote Sens.,vol.39(2) :pp303-308
Earner,K.,Arce G R.,1992． Permutation filters:A class of nonlinear filters based on set permutations.IEEE Trans.Signal Processing,vol.42(4) :pp.782-798．
Barnsley,M.J.and Barr,S.L.,1996． Informing urban land-use from satellite sensors using kernel-based spatial reclassification.Photogrammetry Engineering & Remote Sensing,vol.62:949-958．
Benediktsson,J.A.and Sveinsson J.R.,1997． Feature extraction for multisource data classification with artificial neural networks.Int.J.Remote Sensing,vol.18,no.4,727-740
Benediktsson,J.A.,Swain P.H.,Ersoy O.K.,1990． Neural Network approaches versus statistical methods in classification of multisource remote sensing data.IEEE Trans.Geosci.Remote Sensing.Vol.28(4) :pp.540-552．
Bethke A D.Genetic Algorithms as Function Optimizers.Dissertation Abstracts International, 1981,41(9) ,3503B(University Microfilms No.8106101)
Bovik A.C.,Huang T.,and Munson D.C,1983． A generalization of median filtering using linear combinations of order statistics.IEEE Trans.Acoust.Speech Signal Processing,31,pp.1342-1350．
Bovik,A.C.,Clark,M.and Geisler,W.S.,1990． Multi-channel texture analysis using localized spatial features.IEEE Trans.P.A.M.I.,vol.12(1) :55-73．
Brill,F.,Brown,D.,and Martin,W.,1992． Fast genetic selection of features for neural network classifiers.IEEE Transactions on Neural Networks,3(2) :pp.324-328．
Brownrigg D.R.K.,1984． The weighted median filter.Comm.Assoc.Comput.Mach.,27,pp.807-818．
Bruce,L.M.,Roger C.L.,and Li J.,2002． Dimensionality Reduction of Hyperspectral Data Using Discrete Wavelet Transform Feature Extraction.IEEE Transaction on Geoscience and Remote Sensing,vol.40(10) :pp.2331-2338
Brumby,S.P.,Harvey N.R.,Perkins S.,Porter R.B.,Szymanski J.J.,Theiler J.,and Bloch J.J.,A genetic algorithm for combining new and existing image processing tools for multispectral imagery,2000,Proc.SPIE 4049,480．
Brumby,S.P.,Theiler J.,Perkins S.J.,Harvey N.R.,Szymanski J.J.,Bloch J.J.,and Mitchell M., Investigation of Feature Extraction by a Genetic Algorithm,Proc.SPIE 3812,pp 24-31,1999．
Carnahan,W.H.and Zhou,G,1986． Fourier transform techniques for the evaluation of the Thematic Mapper line spread function[J].Photogramm.Eng.Remote Sensing,52(5) :639-648．
Carpenter,G A.,Gjiaja M.N.,Gopal S.,and Woodcock C.E.,1997． ART neural networks for remote sensing:Vegetation classification from Landsat TM and terrain data.IEEE Trans. Geosci.Remote Sens.,vol.35(2) :pp308-325．
Carpenter,G A.,Gopal S.,Macomber S.,Martens Siegfriens,and Woodcock C.E.,1999． A neural network method for mixture estimation for vegetation mapping.Remote Sens.Environ. Vol.70:pp138-152．
Castleman K.R.,1996． Digital Image Processing.Prentice Hall Inc.,Upper Saddle River,NJ.
Cihlar,J.,2000． Land cover mapping of large areas from satellites:status and research priorities.
Int.J.Remote Sensing,vol.21(6&7) :pp.1093-1113
Cloutis,E.A.,1996． Hyperspectral geological remote sensing:evaluation of analytical techniques. Int.J.Remote Sensing,17(12) :pp.2215-2242．
Cohen,J.,1960． A coefficient of agreement for nominal scales,Educ.Psychol.Measurement. 20(1) :37-46．
Coifman,R.D.,et al,1992． Wavelet and signal processing:in Ruskai ed.:Wavelet and Their Application,Jones and Bartlett Publishers.
Congalton,R.G,1991． A Review of Assessing the Acurracy of Classifications of Remotely Sensed Data.Remote Sens.Environ.Vol.37:pp35-46
Gormen,T,Leiserson,C.,and Rivest,R.,1990． Introduction to Algorithms.MIT Press,Cambridge,MA.
Cost,S.and Salzberg,S.,1993． A weighted nearest neighbor algorithm for learning with symbolic features.Machine Learning,10(1) :57-78．
Coyle,E.J.,Lin J.H.,1988． Stack filters and the mean absolute error criterion.IEEE Trans. Acoust.Speech Signal Processing,36(8) ,pp.1244-1254．
Coyle,E.J.,Lin J.H.,and Gabbouj M.,1989． Optimal stack filtering and the estimation and structural approaches to image processing.IEEE Trans.Acoust.Speech Signal Processing, 37(12) ,pp.2037-2066．
Crist,E.P.and Cicone,R.C.,1984． A physically-based transformation of Thematic Mapper data-the TM Tassedlled Cap.IEEE Transaction on Geoscience and Remote Sensing,50,497-503
Cross,A.M.,1988． Detection of circular geological features using the hough transform.Int.J. Remote Sensing,vol.9(9) :1519-1528．
Curran,P.J.,1988． The semi-variogram in remote sensing:an introduction.Remote Sensing of Environment,24:pp.493-507．
Dash,M.and Liu,H.,1997． Feature selection for classification.Intelligent Data Analysis,1(3) .
Dawson,T.P.and Curran,P.J.,1998． A new technique for interpolating the reflectance of red edge position." Int.J.Remote Sensing,vol.19:2133-2139．
Deering,D.W.(1978) ,Rangeland reflectance characteristics measured by aircraft and spacecraft sensors.Ph.D.Dissertation,Texas A & M University,College Station,TX,338 pp.
DeFries,R.S,et al,1995． Global Discrimination of Land Cover Types from Metrics Derived from AVHRR Pathfinder Data.Remote Sens,of Environ.,vol 54:pp.209-222
DeFries,R.S.,Hansen M.C.,Towshend J.R.G,1998． Global land cover classifications at 8 km spatial resolution:the use of training data derived from landsat imagery in decision tree classifiers.Int.J.Remote Sensing,vol.19:pp.3141-3168
Demetriades-Shah,T.H.,Steven,M.D.,Clark,J.A.,1990 "High Resolution Derivatives Spectra in Remote Sensing",Remote Sens.Environ.,vol.33:55-64．
Devijver,P.,1982． Pattern Recognition:A Statistical Approach.Prentice Hall.
Doak,J.,1992． An evaluation of feature selection methods and their application to computer security.Technical Report CSE-92-18,Department of Computer Science,University of California,Davis,CA.
Dutra,L.V.,and Huber,R.,1999,Feature extraction and selection for ERS-1/2 InSAR classi. cation.International Journal of Remote Sensing,20,993-1016．
Florencio,D.A.F,Schafer R W,1994． Decision-based median filter using local signal statistics.In: Proc SPIE Symp.Visual Comm.Image Processing,Chicago:pp268-275
Foley,J.D.,A.van Dam,S.K.Feiner and J.F.Hughes,1990． Computer Graphics:Principles and Practice (second edition).Addison-Wesley Publishing Company.
Foroutan,I.and Sklansky,J.,1987． Feature selection for automatic classification of non-gaussian data.IEEE Transactions on Systems,Man and Cybernetics,17:187-198．
Forster,B.C.and Best P.,1994． Estimation of SPOT P-mode point spread function and derivation of a deconvolution filter[J].ISPRS Journal of Photogrammetry and Remote Sensing,49(6) :32-42．
Frankot,R.T.and Chellapa,R.,1987． Lognormal random-field models and their applications to radar image systhesis.IEEE Transactions on Geoscience and Remote Sensing,25:pp.195-207．
Fu,K.S.,1982,Application of pattern recognition to remote sensing.In Applications of Pattern Recognition,edited by K.S.Fu (Boca Raton,Florida:CRC Press),Chapter 4．
Fukunaga,K.,1990． Introduction to Statistical Pattern Recognition.Academic Press,New York.Goldberg,D E.Optimal Initial Population Size for Binary-coded Genetic Algorithms (TCGA Report No.85001) .University of Alabama,The Clearing-house for Genetic Algorithms,1985．
Goldberg,D.E.,1989． Genetic Algorithms in Search,Optimization,and Machine Learning. Addison-Wesley,New York.
Gong,P.and Howarth P.J.,1990． The use of structure information for improving land-cover classification accuracies at the rural-urban fringe.Photogrammetric Engineering and Remote Sensing,56(1) :67-73
Gong,P.and Howarth P.J.,1992． Frequency-based contextual classification and grey-level vector reduction for landuse identification.Photogrammetric Engineering and Remote Sensing, 58(4) :423-437
Gong,P.,1994． Reducing boundary effects in a kernel-based classifier.Int.J.Remote Sensing, 15(5) :pp.1131-1139
Goodenough,D.G,Narenda,P.M.,and O'Neill,K.,1978,Feature subset selection in remote sensing.Canadian Journal of Remote Sensing,4,143-148．
Gopal,S.,Woodcock C.E.,Strahler,A.H.,1999． Fuzzy neural network classification of global land cover from a 1 degree AVHRR data set.Remote Sens.Environ.Vol.67:pp230-243．
Green,A.A.,Berman,M.,Switzer,P.and Craig,M.D.,1988． A transformation for ordering multispectral data in terms of image quality with implications for noise removal.IEEE Transaction on Geoscience and Remote Sensing,26,65-74
Hancock,P.J.B.,1992． Genetic algorithms and permutation problems:a comparison of recombination operators for neural net structure specification.Proceedings of the Int. Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN-92) ,pp. 108-122． IEEE Computer Society Press,Los Alamitos,CA.
Hansen,M.C.,Reed B.,2000． A comparison of the IGBP DISCover and University of Maryland 1km global land cover products.Int.J.Remote Sensing,vol.21(6&7) :pp.1365-1373
Haralick,R.M.,Shanmugam,K.and Dinstein,I.,1973． Texture feature for image classification. IEEE Transaction on Systems,Man,and Cybernetics,3,610-621
Hardie,R.C.,Barner K.E.,1994． Rank conditioned rank selection filters for signal restoration. IEEE Trans.Image Processing,3(2) :pp.192-206
Hardie,R.C.,Boncelet C.G,1993． LUM filters:A class rank order based filters for smoothing
and sharping.IEEE Trans.Signal Processing.41(3) :pp.1061-1076．
Harvey,N.R.,Perkins S.,Brumby S.P.,Theiler J.,Porter R.B.,Young A.C.,Varghese A.K., Szymanski J.J.,and Bloch J.J.,Finding Golf Courses:The Ultra High Tech Approach, Proceedings of EvoIASP 2000:The Second European Workshop on Evolutionary Computation in Image Analysis and Signal Processing,Edinburgh,April 17th,2000．
Harvey,N.R.,Theiler J.,Brumby S.P.,Perkins S.,Szymanski J.J.,Bloch J.J.,Porter R.B.,Galassi M.,and Young A.C.,2002． Comparison of GENIE and Conventional Supervised Classifiers for Multispectral Image Feature Extraction.IEEE Transaction on Geosecience and Remote Sensing,vol.40(2) :pp.393-404．
He,D.C.,Wang L.,1994． Classification spectral et texturale des Donne'ss d'Images SPOT em Milieu Urban.Int.J.Remote Sensing,vol.15:2145-2152
Herrera,F.,Lozano M.,Verdegay J.L.,Tackling Real-Coded Genetic Algorithms:Operators and Tools for Behavioural Analysis.NEC Research Index,http://citeseer.nj.nec.com/
Hertz,J.,Krogh A.,Palmer R.,1991． A Introduction to the Theory of Neural Computation. Addison-Wesley,Readings,CA.
Hevenor,R.A.,1985． Third-order co-occurance texture analysis applied to samples of high resolution Synthetic Aperture Radar Imagery.U.S.Army Corps of Engineers.Engineering Topographic Laboratory.Fort Belvoir,Virginia 22060-5546,p32,1985．
Hlavka,C.A.,1987． Land-use mapping using edge density texture measures on Thematic Mapper Simulator data.IEEE Trans.Geosci.& Remote Sensing,GE-25(1) :104-107．
Holland,J H.Genetic Algorithms and Classifier Systems:Foundations and Future Directions. Genetic Algorithms and Their Applications:Proceedings of the Second International Conference on Genetic Algorithms.1987． 82-89
Holland,J.H.,1975． Adaptation in Natural and Artificial Systems.The University of Michigan Press.
Hsu,Pai-Hui,Tseng Yi-Hsing,1999． Feature Extraction for Hyper Spectral Image.Asian Conference On Remote Sensing,November 22-25,1999,Hong Kong,China
Hsu,S.,1978． Texture tone analysis for automated land use mapping.Photogrammetry Engineering & Remote Sensing,vol.44(11) :1393-1404．
Huete,A.R.(1988) ,A soil adjusted vegetation index (SAVI),Remote Sens.Environ.,25:295-309．
Huguenin,R.L.,and Jones J.L.,1986． Intelligent information extraction from reflectance spectra: absorption band position.Journal of Geophysical Research,1986,91:pp.9585-9598．
Hush,D.R.,Home B.G,1993． Progress in supervised neural networks.IEEE Signal Processing Magazine,vol.10(1) :8-39．
Hwang,H.,Haddad R.A.,1995． "Adaptive median filters:new algorithms and results," IEEE Trans.Image Processing,4(4) :pp.499~502．
Irons,J.R.and Petersen,G.W.,1981． Texture transforms of remote sensing data.Remote Sens,of Environ.,11:359-370．
Jackson,R.D.,P.N.Slater,and P.J.Pinter,1983． Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres.Remote Sensing of the Environment,15:187-208．
Jensen,J.R.,1996,Introductory Digital Image Processing:A Remote Sensing Perspective London: Prentice Hall.
Jia,Xiuping and Richards,J.A.,1999． Segmented principal components transformation for
efficient hyperspectral remotesensing image display and classification.IEEE Trans.On Geosci.And Remote Sensing,vol.37(1) :538-542．
John,G,Kohavi,R.,and Pfleger,K.,1994． Irrelevant features and the subset selection problem.In Proceedings of the Eleventh International Conference on Machine Learning,pp.121-129, New Brunswick,NJ.Morgan Kaufmann.
Jordan,C.F.(1969) ,Derivation of leaf area index from quality of light on the forest floor,Ecology, 50:663-666．
Justusson,B.I.,1981． "Median filtering:statistics properties," in Two-Dimensional Digital Signal Processing,II:Transforms and Median Filters,Vol.42,New York:Springer Verlag,pp.161-196．
Kailath,T,1967,The divergence and Bhattacharyya distance measures in signal selection.IEEE T ransactions on Communications T echnology,COM-15,52-60．
Kaufman,Y.J.and Tanre,D.(1992) ,Atmospherically resistant vegetation index (ARVI)for EOS-MODIS,IEEE Trans.Geosci.Remote Sensing,30:261-270．
Kauth,R.J.and Thomas,G,1976． The tasseled cap-a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat,Proceedings of the Symposium on Machine Processing of Remotely Sensed Data,1976,Purdue Universtiy,West Lafayette,IN,pp.4 B-41-4 B-51．
Kim,S.R.,Efron A.,1995． "Adaptive robust impulse noise filtering," IEEE Trans.Signal Processing,43(8) :pp.1855-1866．
Kim,V,Yaroslavskii L.,1986． Rank algorithms for picture processing.Computer Vision,Graphics, Image Processing.35(2) :pp.234-258
Kira,K.and Rendell,L.,1992． A practical approach to feature selection.In Proceedings of the Ninth International Conference on Machine Learning,pp.249-256． Morgan Kaufmann.
Ko,S.J.,Lee Y.H.,1991． Center weighted median filters and their applications to image enhancement.IEEE Trans.Circuits Syst.38(9) :pp.983-993．
Kononenko,R.,and Agyepong,K.,1996． On lateral connections in feed-forward neural networks. In Proceedings of the International Conference on Neural Networks,pp.13-18．
Lampinen,J.,and Oja,E.,1995,Distortion tolerant pattern recognition based on selforganizing feature extraction.IEEE Transactions on Neural Networks,6,539-547．
Landgrebe,D.,1998． Information extraction principles and methods got multispectral and hyperspectral image data,in C.H.Chen (editor),Information Processing for Remote Sensing. Rover Edge,NJ:World Scientific Publishing.
Langley,P.,1994． Selection of relevant features in machine learning.In Proceedings of the AAAI Fall Symposium on Relevance,pp.1-5,New Orleans,LA.AAAI Press.
Lee,C.and Landgrebe,D.A.,1991． Fast likelihood classification.IEEE Trans.Geosci.& Remote Sensing,vol.29(4) :509-517
Lee,C.,and Landgrebe,D.A.1993b,Decision boundary feature extraction for non-parametric classifiers.IEEE Transactions on Systems,Man and Cybernetics,23,433-444．
Lee,C.,and Landgrebe,D.A.,1992,Decision boundary feature selection for neural networks. Proceedings of the I.E.E.E.International Conference on Systems,Man and Cybernetics (New York:I.E.E.E.Press),pp.1053-1057．
Lee,C.,and Landgrebe,D.A.,1993a,Feature extraction and classi(?) cation algorithms for high dimensional data.Technical Report TR-EE 93-1,School of Electrical Engineering,Purdue
University,West Lafayette,Indiana,U.S.A.
Lee,C.,and Landgrebe,D.A.,1993c,Feature extraction based on decision boundaries.IEEE Transactions on Pattern Analysis and Machine Intelligence,15,388-400．
Lee,J.B.,Woodyatt,A.S.and Herman,M.,1990． Enhancement of high spectral resolution remote sensing data by noise-adjusted principal components transform.IEEE Transaction on Geoscience and Remote Sensing,28,295-304．
Li,J.,Bruce,L.M.and Barnett,J.,2001． Automated detection of Pueraria Montana(kudzu) through Haar analysis of hyperspectral reflectance data.Proceedings of IGARSS, pp.2247-2249,July 2001．
Li,X.,Strahler.A.H.,1985． Geometric-Optical Modeling of a Conifer Forest Canopy.IEEE Transactions on Geoscience and Remote Sensing.46(12) :1563-1573
Liang,S.and Strahler A.,1993． An analytic BRDF model of canopy radiative transfer and its inversion.IEEE Transaction on Geoscience and Remote Sensing,31(5) :1081-1092．
Lin,H.M.,Willson A.N.,1988． Median Fiters with adaptive length.IEEE CAS1,35(6) :pp675-690
Lin,J.H.,Sellke T.M.,Coyle E.J.,1990． Adaptive stack filtering under the mean absolute error criterion.IEEE Trans.Acoust.Speech Signal Processing,vol.38(6) :pp.938-954．
Liu,H.Q.,and Huete,A.R.(1995) ,A feedback based modification of the NDVI to minimize canopy background and atmospheric noise,IEEE Trans.Geosci.Remote Sensing, 33:457-465．
Liu,H.,and Setiono,R.,1996a.Feature selection and classification-a probabilistic wrapper approach.In Proceedings of the Ninth International Conference on Industrial and Engineering Applications of AI and ES.
Liu,H.,and Setiono,R.,1996b.A probabilistic approach to feature selection-a filter solution.In Proceedings of the Thirteenth International Conference on Machine Learning.Morgan Kaufmann.
Lloyd,D.,1990． A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery.Int.J.Remote Sensing,1990 vol.11(12) :2269-2279
Loveland,T.R.,et al,1991． Development of a Land-Cover Characteristics Database for the Conterminous U.S..Photogrammetry Engineering & Remote Sensing,vol 57(11) : pp.1453-1463
Mandelbrot,B.B.,1967． How long is the coast of Britain? Statistical self-similarity and fractional dimension,Science 156,636-638
Mandelbrot,B.B.,1977． Fractals:Form,Chance and Dimension.San Francisco,CA:Freeman.
Mao,J.,and Jain,A.K.,1995,Artificial neural networks for feature extraction and multivariate data projection.I.E.E.E.Transactions on Neural Networks,6,296-317．
Mather,P.M.,1999,Computer Processing of Remotely-Sensed Images:An Introduction,2nd edn (Chichester,UK:John Wiley).
Mausel,P.W.,Kramber,W.J.,and Lee,J.K.,1990,Optimum band selection for supervised classi. cation of multispectral data.Photogrammetric Engineering and Remote Sensing,56,55-60．
Mayaux,P.and Lambin,E.F.,1997． Tropical forest area measured from global land-cover classifications:Inverse calibration models based on spatial textures.Remote Sens.Environ., 59:29-43．
McGillem,C.D.,Anuta,P.E.,Malalaret,E.and Yu,K.B.,1983． Estimation of a remote sensing
system point spread function from measured imagery[A].Proc.Symp.Machine Processing of Remotely Sensed Data[C],Purdue University,Lafayette,Ind.,1983,pp.62-68．
Michalewicz Z.,1992． Genetic Algorithms + Data Structures=Evolution Programs. Springer-Verlag,New York.
Miranda,F.and MacDonald,J.,1992． Application of the semivariogram textural classifier(STC) for vegetation discrimination using SIR-B data of Borneo.Int.J.Remote Sensing,vol.13:2349-2354
Motwani,R.and Raghavan,P.,1996． Randomized algorithms.ACM Computing Surveys,28(1) :33-37．
Narendra,P.,and Fukunaga,K.,1977． A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers,26:917-922．
Neilsen,A.A.,1994． Analysis of regularly and irregularly sampled spatial,multivariate,and multi-temporal data.Ph.D.thesis,Institute of Mathematical Modeling,Technical University of Denmark,Lyngsby,Denmark.
Neilsen,A.A.,Conradsen,k.and Simpson,J.J.,1998． Multivariate alteration detection(MAD) and MAP post-processing in multispectral,bi-temporal image data:new approaches to change detection studies,Remote Sensing of Environment,64:1-19．
Nilsson N.J.,1998． Artificial Intelligence:A New Synthesis.Morgan Kaufmann Publishers,Inc: San Francisco,CA.
Oja,E.,1995,PCA,ICA,and non-linear Hebbian learning.Proceedings of the International Conference on Artificial Neural Networks (ICANN '95) ,Paris,France,9-13 October 1995, pp.89-94．
Olsen,E.R.,R.D.Ramsey,and D.S..Winn.1993． A Modified Fractal Dimension as a Measure of Landscape Diversity.Photogrammetric Engineering & Remote Sensing.Vol. 59(10) :1517-1520
Pal S.K.,Bandyopadhyay S.,Murthy C.A.,2001． Genetic classifier for remotely sensed images: comparison with standard methods.Int.J.Remote Sensing.Vol.22(13) :pp2545-2569
Perkins S.,Theiler J.,Brumby S.P.,Harvey N.R.,Porter R.B.,Szymanski J.J.,and Bloch J.J., GENIE-A Hybrid Genetic Algorithm for Feature Classification in Multi-Spectral Images, 2000,Proc.SPIE 4120．
Philpot,W.D.,1991． The Derivative Ratio Algorithm:Avoiding Atmospheric Effects in Remote Sensing",IEEE Transactions on Geoscience &Remote Sensing,29(3) :350 357．
Punch,W.,Goodman,E.,Pei,M.,Chia-Shun,L.,Hovland,P.,and Enbody,R.,1993． Further research on feature selection and classification using genetic algorithms.In Proceedings of the International Conference on Genetic Algorithms,pp.557-564． Springer.
Quinlan,R.,1993． C4． 5:Programs for Machine Learning.Morgan Kaufmann,San Mateo,CA.Rao,C.R.,1965． Linear Statistical Inference and Its Applications,John Wiley and Sons,New York,1965．
Richards,J.A.,1993． Remote Sensing Digital Image Analysis:An introduction,Springer-Verlag Berlin Heidelberg,Second Edition.
Richardson,A.J.and Wiegand,C.L.(1977) ,Distinguishing vegetation from soil background information,Photogramm.Eng.Remote Sens.,43:1541-1552．
Richeldi,M.,and Lanzi,P.,1996． Performing effective feature selection by investigating the deep structure of the data.In Proceedings of the Second International Conference on Knowledge
Discovery and Data Mining,pp.379-383． AAAI Press.
Ripley,B.,1996． Pattern Recognition and Neural Networks.Cambridge University Press,New York.
Rissanen,J.,1978． Modelling by shortest data description.Automatica,14:465-471．
Ruiz,C.P.and Lopez F.J.A,2002． Restoring SPOT images using PSF-derived deconvolution filters[J].Int.J.Remote Sensing,23(12) :2379-2391．
Running S.W.,et al,1995． A Remote Sensing Based Vegetation Classification Logic for Global Land Cover Analysis[J].Remote Sens,of Environ.,vol 51:pp.39-48
Russell,S.,and Norvig,P.,1995． Artificial Intelligence:A Modern Approach.Prentice Hall, Englewood Cliffs,NJ.
Ryherd,S.and Woodcock,C.,1996． Combining spectral and textural data in the segmentation of remotely sensed images.Photogrammetry Engineering & Remote Sensing, vol.62(2) :181-194．
Sali,E.and Wolfson,H.,1992． Texture classification in aerial photographs and satellite data.Int.J. Remote Sensing,vol.13:3395-3408
Schowengerdt,R.A..Remote Sensing:Models and Methods for Image Processing (Second Edition).San Diego:Academic Press,1997．
Sellers,P.J.,Dickinson,R.E.,Randall,D.A.,Betts,A.K.,Hall,F.G,Mooney,H.A.,Nobre,C. A.,Sato,N.,Field,C.B.,and Henderson-Sellers,A.,1997． Modeling the exchanges of energy,water,and carbon between continents and atmosphere.Science,275:pp.502-509
Sheinvald,J.,Dom,B.,and Niblack,W.,1990． A modeling approach to feature selection.In Proceedings of the Tenth International Conference on Pattern Recognition,pp.535-539．
Shi Z.,Zhang D.S.,Kouri D.J.,and Hoffman D.K..Nonlinear Quincunx Filters.IEEE Transaction on PAMI (submitted).
Siedlecki,W.and Sklansky,J.,1988． On automatic feature selection.International Journal of Pattern Recognition,2:197-220．
Siedlecki,W.,and Sklansky,J.,1989． A note on genetic algorithms for large-scale feature selection. IEEE Transactions on Computers,10:pp.335-347．
Skalak,D.,1994． Prototype and feature selection by sampling and random mutation hill-climbing algorithms.In Proceedings of the Eleventh International Conference on Machine Learning, pp.293-301,New Brunswick,NJ.Morgan Kaufmann.
Story,M.and Congalton,R.(1986) .Accuracy assessment:a user's perspective.Photogramm.Eng. Remote Sens.vol.52(3) :pp397-399．
Sun,T.,Gabbouj M.,and Neuvo Y.,1992． Deterministic properties of center weighted median filters.Proc.1992 IEEE Int.Conf.Commun.Technol.Tsinghua Campus,Beijing,China.
Swain,P.H.and Davis,S.M.(editors),1978． Remote Sensing:The Quantitative Approach.New York:McGrowHill.
Switzer,P.and Green,A.A.,1984． Min/Max Autocorrelation Factors for Multivariate Spatial Imagery,Tech.Report No.6,Department of Statistics,Stanford University,Stanford,CA.
Tadjudin,S.and D.Landgrebe,1998． "Classification of High Dimensional Data with Limited Training Samples",PhD Thesis and School of Electrical & Computer Engineering Technical Report TR-ECE 98-8．
techniques.International Journal of Remote Sensing,vol.17:2215-2242．
Theiler J.,Harvey N.R.,Brumby S.P.,Szymanski J.J.,Alferink S.,Perkins S.,Porter R.,and
Bloch J.J.,"Evolving Retrieval Algorithms with a Genetic Programming Scheme",1999, Proc.SPIE 3753．
Thomas,I.L.,Benning,V.M.,and Ching,N.P.,1987a,Classi.cation of Remotely Sensed Images London:Adam Hilger.
Thomas,I.L.,Ching,N.P.,Benning,V.M.,and D'Aguanno,J.A.,1987b,A review of multi-channel indices of class separability.International Journal of Remote Sensing,8, 331-350．
Townshend,J.,Justice C.,Li W.,Gurney C.,McManus J.,1991． Global Land Cover Classification by Remote Sensing:Present Capabilities and Future Possibilities.Remote Sens,of Environ., vol 35:pp.243-255．
Townshend,J.R.G,Justice,C.O.,Kalb,V.,Characterization and classification of South American land cover types using satellite data.International Journal of Remote Sensing, 1987,7:1395-1416．
Tso,B.and Mather,P.M.,2001． Classification methods for remotely sensed data.Taylor-Francis Inc.New York,NY,USA.
Tso,B.,and Mather,P.M.,1999,Crop discrimination using multi-temporal SAR imagery. International Journal of Remote Sensing,20,2443-2460．
Tucker,C.J.,Townshend,J.R.G and Goff,T.E.1984． Continental land cover classification using meteorological satellite data.Science,vol.227:pp.369-375．
Tucker,C.J.,W.W.Newcomb,S.O.Los,and S.D.Prince,1991． Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989． International Journal of Remote Sensing,12:1113-1115．
Tucker,C.J.1979． Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment,8:127-150．
Tukey,J.W.,Exploratory data analysis.Addison-Wesley,New York,1971．
Unser,M.and Eden,M.,1989． Multiresolution feature extraction and selection for texture segementation.IEEE Trans.P.A.M.I.,vol.11(7) :717-728．
Vafaie,H.,and De Jong,K.,1993． Robust feature selection algorithms.In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence,pp.256-363．
Wang,Z..,Zhang D.,1998． "Restoration of impulse noise corrupted images using long-range correlation," IEEE Signal Processing Letter,Vol.5:pp.4-7．
Wang,F.,1990． Fuzzy supervised classification of remote sensing images.IEEE Trans.Geosci.& Remote Sensing,vol.28(2) :194-201．
Wickerhauser,V.,1991． INRIA lectures on wavelet packet algorithms.
Wendt,P.D.,Coyle E.J.,and Gallagher N.C.,1986． Stack filters.IEEE Trans.Acoust.Speech Signal Processing,vol.34(8) :pp.898-911．
Woodcock,C.E.and Strahler A.H.,1983． Characterising spatial patterns in remotely sensed data. Proceedings of the 17th International Symposium on Remote Sensing of Environment.Ann Arbor,MI:University of Michigan,pp.839-852．
Yang,J.,Parekh,R.,and Honavar,V.,1998． DistAI:An Inter-pattern Distance-based Constructive Learning Algorithm.Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (UJCNN'98) ,Anchorage,AK.May 4-8,1998． pp.2208-2213．
Yao,X.,1999． Evolving artificial neural networks.Proceedings of the IEEE,vol.87(9) ,pp. 1423-1447．
Yli-Harja O.,Stola J.,and Neuvo Y.,1991． Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation.IEEE Trans.Signal Processing,vol.39(2) :pp.395-410．
You,X.,Grebbin G,1995． A robust adaptive estimator for filtering noise in images,IEEE Trans. Image Processing,4(5) :pp.693~699．
Young,T.Y.and Fu K.S.,1986． Handbook of Pattern Recognitions and Image Processing,College of Engineering,University of Miami,.Coral Gables,Florida.
Zenzo,S.D.,et al,1987a.Gaussian maximum likelihood and contextual classification algorithm for multicrop classification.IEEE Trans.Geosci.& Remote Sensing,GE-25(6) :805-814．
Zenzo,S.D.,et al,1987b.Gaussian maximum likelihood and contextual classification algorithm for multicrop classification experiments using Thematic Mapper and Multispectral Scanner Sensor data.IEEE Trans.Geosci.& Remote Sensing,GE-25(6) :815-824．
Zhang,D.,Wang Z.,1997． Impulse noise detection and removal using fuzzy techniques. Electronics letters,33(5) :pp.378-379
胡宝新、李小文、朱重光、Alan Straler。大倾角光学遥感中大气点扩散函数的近似模型。中国图像图形学报，1996，1(1)：19-29。
胡昌华、张军波、夏军、张伟，1999。基于MATLAB的系统分析与设计——小波分析。西安：西安电子科技大学出版社。
李小文、王锦地，1995。植被光学遥感模型与植被结构参数化。北京：科学出版社。
刘建贵，1999。高光谱城市地物及人工目标识别与提取。中国科学院遥感应用研究所博士论文。北京：中国科学院遥感应用研究所。
刘明亮，2001。中国土地利用/土地覆盖变化与陆地生态系统植被碳库和生产力研究。中国科学院遥感应用研究所博士论文。北京：中国科学院遥感应用研究所。
刘强，2002。地表组分温度反演方法及遥感像元的尺度结构。中国科学院遥感应用研究所博士论文。
刘正军、王长耀、延昊等，2003。基于Fuzzy ARTMAP神经网络的高分辨率图像土地覆盖分类及其评价。中国图像图形学报，8(2)：151-154。
罗迪，1999．基于AVHRR和地学空间数据的中国土地覆盖分类．中国科学院遥感应用研究所硕士论文。北京：中国科学院遥感应用研究所。
牛铮、朱重光、王长耀，1997。斜视角度下大气交叉辐射影响分析。遥感学报，1997，1(2)：88-93。
潘正均、康立山、陈毓屏。演化计算。北京：清华大学出版社，1998。
浦瑞良、宫鹏，2000。高光谱遥感及其应用。北京：高等教育出版社。
沈清、汤霖，1991。模式识别导论。长沙：国防科技大学出版社。
史培军、宫鹏等，2000。土地利用/覆盖变化研究的方法与实践。北京：科学出版社。
孙即祥、王晓华、钟山、张帆、史慧敏，2001。模式识别中的特征提取与计算机视觉不变量。北京：国防工业出版社。
杨福生，1999。小波变换的工程分析与应用。北京：科学出版社。
张继贤，李德仁，1996。影像纹理的多尺度分析。环境遥感，1996，11(1)，1-13。
张继贤，1994。影像纹理的多层次分析方法。武汉测绘科技大学博士论文。武汉：武汉测绘科技大学。
赵荣椿、赵忠明、崔更生，1996。数字图像处理导论。西安：西北工业大学出版社。
Abreu, E, Lightone M, Mitra S K et al, 1996. A new efficient approach for the removal of impulse noise from highly corrupted images. IEEE IP,5(6):pp.1012-1025
Aha, D. W., and Bankert, R. L., 1996, A comparative evaluation of sequential feature selection algorithms. In Learning from Data: Arti. cial Intelligence and Statistics V, edited by D. Fisher and J.-H. Lenz (New York: Springer Verlag), pp. 199-206.
Almuallim, H. and Dietterich, T., 1994. Learning Boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1-2): 279-305.
Anys, H. et al, 1994. Texture analysis for the mapping of urban areas using airborne MEIS-Ⅱ images, the first International airborne remote sensing conference and exhibition, Strasbourg, France, 11-15 Sept. 1994.
Atkinson, P. M. and Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers and Geosciences, 26: pp.361-371.
Bandyopadhyay,S.,Pal S.K.,2001． Pixel classification using variable string genetic algorithms with chromosome differentiation.IEEE Trans.Geoscience & Remote Sens.,vol.39(2) :pp303-308
Earner,K.,Arce G R.,1992． Permutation filters:A class of nonlinear filters based on set permutations.IEEE Trans.Signal Processing,vol.42(4) :pp.782-798．
Barnsley,M.J.and Barr,S.L.,1996． Informing urban land-use from satellite sensors using kernel-based spatial reclassification.Photogrammetry Engineering & Remote Sensing,vol.62:949-958．
Benediktsson,J.A.and Sveinsson J.R.,1997． Feature extraction for multisource data classification with artificial neural networks.Int.J.Remote Sensing,vol.18,no.4,727-740
Benediktsson,J.A.,Swain P.H.,Ersoy O.K.,1990． Neural Network approaches versus statistical methods in classification of multisource remote sensing data.IEEE Trans.Geosci.Remote Sensing.Vol.28(4) :pp.540-552．
Bethke A D.Genetic Algorithms as Function Optimizers.Dissertation Abstracts International, 1981,41(9) ,3503B(University Microfilms No.8106101)
Bovik A.C.,Huang T.,and Munson D.C,1983． A generalization of median filtering using linear combinations of order statistics.IEEE Trans.Acoust.Speech Signal Processing,31,pp.1342-1350．
Bovik,A.C.,Clark,M.and Geisler,W.S.,1990． Multi-channel texture analysis using localized spatial features.IEEE Trans.P.A.M.I.,vol.12(1) :55-73．
Brill,F.,Brown,D.,and Martin,W.,1992． Fast genetic selection of features for neural network classifiers.IEEE Transactions on Neural Networks,3(2) :pp.324-328．
Brownrigg D.R.K.,1984． The weighted median filter.Comm.Assoc.Comput.Mach.,27,pp.807-818．
Bruce,L.M.,Roger C.L.,and Li J.,2002． Dimensionality Reduction of Hyperspectral Data Using Discrete Wavelet Transform Feature Extraction.IEEE Transaction on Geoscience and Remote Sensing,vol.40(10) :pp.2331-2338
Brumby,S.P.,Harvey N.R.,Perkins S.,Porter R.B.,Szymanski J.J.,Theiler J.,and Bloch J.J.,A genetic algorithm for combining new and existing image processing tools for multispectral imagery,2000,Proc.SPIE 4049,480．
Brumby,S.P.,Theiler J.,Perkins S.J.,Harvey N.R.,Szymanski J.J.,Bloch J.J.,and Mitchell M., Investigation of Feature Extraction by a Genetic Algorithm,Proc.SPIE 3812,pp 24-31,1999．
Carnahan,W.H.and Zhou,G,1986． Fourier transform techniques for the evaluation of the Thematic Mapper line spread function[J].Photogramm.Eng.Remote Sensing,52(5) :639-648．
Carpenter,G A.,Gjiaja M.N.,Gopal S.,and Woodcock C.E.,1997． ART neural networks for remote sensing:Vegetation classification from Landsat TM and terrain data.IEEE Trans. Geosci.Remote Sens.,vol.35(2) :pp308-325．
Carpenter,G A.,Gopal S.,Macomber S.,Martens Siegfriens,and Woodcock C.E.,1999． A neural network method for mixture estimation for vegetation mapping.Remote Sens.Environ. Vol.70:pp138-152．
Castleman K.R.,1996． Digital Image Processing.Prentice Hall Inc.,Upper Saddle River,NJ.
Cihlar,J.,2000． Land cover mapping of large areas from satellites:status and research priorities.
Int.J.Remote Sensing,vol.21(6&7) :pp.1093-1113
Cloutis,E.A.,1996． Hyperspectral geological remote sensing:evaluation of analytical techniques. Int.J.Remote Sensing,17(12) :pp.2215-2242．
Cohen,J.,1960． A coefficient of agreement for nominal scales,Educ.Psychol.Measurement. 20(1) :37-46．
Coifman,R.D.,et al,1992． Wavelet and signal processing:in Ruskai ed.:Wavelet and Their Application,Jones and Bartlett Publishers.
Congalton,R.G,1991． A Review of Assessing the Acurracy of Classifications of Remotely Sensed Data.Remote Sens.Environ.Vol.37:pp35-46
Gormen,T,Leiserson,C.,and Rivest,R.,1990． Introduction to Algorithms.MIT Press,Cambridge,MA.
Cost,S.and Salzberg,S.,1993． A weighted nearest neighbor algorithm for learning with symbolic features.Machine Learning,10(1) :57-78．
Coyle,E.J.,Lin J.H.,1988． Stack filters and the mean absolute error criterion.IEEE Trans. Acoust.Speech Signal Processing,36(8) ,pp.1244-1254．
Coyle,E.J.,Lin J.H.,and Gabbouj M.,1989． Optimal stack filtering and the estimation and structural approaches to image processing.IEEE Trans.Acoust.Speech Signal Processing, 37(12) ,pp.2037-2066．
Crist,E.P.and Cicone,R.C.,1984． A physically-based transformation of Thematic Mapper data-the TM Tassedlled Cap.IEEE Transaction on Geoscience and Remote Sensing,50,497-503
Cross,A.M.,1988． Detection of circular geological features using the hough transform.Int.J. Remote Sensing,vol.9(9) :1519-1528．
Curran,P.J.,1988． The semi-variogram in remote sensing:an introduction.Remote Sensing of Environment,24:pp.493-507．
Dash,M.and Liu,H.,1997． Feature selection for classification.Intelligent Data Analysis,1(3) .
Dawson,T.P.and Curran,P.J.,1998． A new technique for interpolating the reflectance of red edge position." Int.J.Remote Sensing,vol.19:2133-2139．
Deering,D.W.(1978) ,Rangeland reflectance characteristics measured by aircraft and spacecraft sensors.Ph.D.Dissertation,Texas A & M University,College Station,TX,338 pp.
DeFries,R.S,et al,1995． Global Discrimination of Land Cover Types from Metrics Derived from AVHRR Pathfinder Data.Remote Sens,of Environ.,vol 54:pp.209-222
DeFries,R.S.,Hansen M.C.,Towshend J.R.G,1998． Global land cover classifications at 8 km spatial resolution:the use of training data derived from landsat imagery in decision tree classifiers.Int.J.Remote Sensing,vol.19:pp.3141-3168
Demetriades-Shah,T.H.,Steven,M.D.,Clark,J.A.,1990 "High Resolution Derivatives Spectra in Remote Sensing",Remote Sens.Environ.,vol.33:55-64．
Devijver,P.,1982． Pattern Recognition:A Statistical Approach.Prentice Hall.
Doak,J.,1992． An evaluation of feature selection methods and their application to computer security.Technical Report CSE-92-18,Department of Computer Science,University of California,Davis,CA.
Dutra,L.V.,and Huber,R.,1999,Feature extraction and selection for ERS-1/2 InSAR classi. cation.International Journal of Remote Sensing,20,993-1016．
Florencio,D.A.F,Schafer R W,1994． Decision-based median filter using local signal statistics.In: Proc SPIE Symp.Visual Comm.Image Processing,Chicago:pp268-275
Foley,J.D.,A.van Dam,S.K.Feiner and J.F.Hughes,1990． Computer Graphics:Principles and Practice (second edition).Addison-Wesley Publishing Company.
Foroutan,I.and Sklansky,J.,1987． Feature selection for automatic classification of non-gaussian data.IEEE Transactions on Systems,Man and Cybernetics,17:187-198．
Forster,B.C.and Best P.,1994． Estimation of SPOT P-mode point spread function and derivation of a deconvolution filter[J].ISPRS Journal of Photogrammetry and Remote Sensing,49(6) :32-42．
Frankot,R.T.and Chellapa,R.,1987． Lognormal random-field models and their applications to radar image systhesis.IEEE Transactions on Geoscience and Remote Sensing,25:pp.195-207．
Fu,K.S.,1982,Application of pattern recognition to remote sensing.In Applications of Pattern Recognition,edited by K.S.Fu (Boca Raton,Florida:CRC Press),Chapter 4．
Fukunaga,K.,1990． Introduction to Statistical Pattern Recognition.Academic Press,New York.Goldberg,D E.Optimal Initial Population Size for Binary-coded Genetic Algorithms (TCGA Report No.85001) .University of Alabama,The Clearing-house for Genetic Algorithms,1985．
Goldberg,D.E.,1989． Genetic Algorithms in Search,Optimization,and Machine Learning. Addison-Wesley,New York.
Gong,P.and Howarth P.J.,1990． The use of structure information for improving land-cover classification accuracies at the rural-urban fringe.Photogrammetric Engineering and Remote Sensing,56(1) :67-73
Gong,P.and Howarth P.J.,1992． Frequency-based contextual classification and grey-level vector reduction for landuse identification.Photogrammetric Engineering and Remote Sensing, 58(4) :423-437
Gong,P.,1994． Reducing boundary effects in a kernel-based classifier.Int.J.Remote Sensing, 15(5) :pp.1131-1139
Goodenough,D.G,Narenda,P.M.,and O'Neill,K.,1978,Feature subset selection in remote sensing.Canadian Journal of Remote Sensing,4,143-148．
Gopal,S.,Woodcock C.E.,Strahler,A.H.,1999． Fuzzy neural network classification of global land cover from a 1 degree AVHRR data set.Remote Sens.Environ.Vol.67:pp230-243．
Green,A.A.,Berman,M.,Switzer,P.and Craig,M.D.,1988． A transformation for ordering multispectral data in terms of image quality with implications for noise removal.IEEE Transaction on Geoscience and Remote Sensing,26,65-74
Hancock,P.J.B.,1992． Genetic algorithms and permutation problems:a comparison of recombination operators for neural net structure specification.Proceedings of the Int. Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN-92) ,pp. 108-122． IEEE Computer Society Press,Los Alamitos,CA.
Hansen,M.C.,Reed B.,2000． A comparison of the IGBP DISCover and University of Maryland 1km global land cover products.Int.J.Remote Sensing,vol.21(6&7) :pp.1365-1373
Haralick,R.M.,Shanmugam,K.and Dinstein,I.,1973． Texture feature for image classification. IEEE Transaction on Systems,Man,and Cybernetics,3,610-621
Hardie,R.C.,Barner K.E.,1994． Rank conditioned rank selection filters for signal restoration. IEEE Trans.Image Processing,3(2) :pp.192-206
Hardie,R.C.,Boncelet C.G,1993． LUM filters:A class rank order based filters for smoothing
and sharping.IEEE Trans.Signal Processing.41(3) :pp.1061-1076．
Harvey,N.R.,Perkins S.,Brumby S.P.,Theiler J.,Porter R.B.,Young A.C.,Varghese A.K., Szymanski J.J.,and Bloch J.J.,Finding Golf Courses:The Ultra High Tech Approach, Proceedings of EvoIASP 2000:The Second European Workshop on Evolutionary Computation in Image Analysis and Signal Processing,Edinburgh,April 17th,2000．
Harvey,N.R.,Theiler J.,Brumby S.P.,Perkins S.,Szymanski J.J.,Bloch J.J.,Porter R.B.,Galassi M.,and Young A.C.,2002． Comparison of GENIE and Conventional Supervised Classifiers for Multispectral Image Feature Extraction.IEEE Transaction on Geosecience and Remote Sensing,vol.40(2) :pp.393-404．
He,D.C.,Wang L.,1994． Classification spectral et texturale des Donne'ss d'Images SPOT em Milieu Urban.Int.J.Remote Sensing,vol.15:2145-2152
Herrera,F.,Lozano M.,Verdegay J.L.,Tackling Real-Coded Genetic Algorithms:Operators and Tools for Behavioural Analysis.NEC Research Index,http://citeseer.nj.nec.com/
Hertz,J.,Krogh A.,Palmer R.,1991． A Introduction to the Theory of Neural Computation. Addison-Wesley,Readings,CA.
Hevenor,R.A.,1985． Third-order co-occurance texture analysis applied to samples of high resolution Synthetic Aperture Radar Imagery.U.S.Army Corps of Engineers.Engineering Topographic Laboratory.Fort Belvoir,Virginia 22060-5546,p32,1985．
Hlavka,C.A.,1987． Land-use mapping using edge density texture measures on Thematic Mapper Simulator data.IEEE Trans.Geosci.& Remote Sensing,GE-25(1) :104-107．
Holland,J H.Genetic Algorithms and Classifier Systems:Foundations and Future Directions. Genetic Algorithms and Their Applications:Proceedings of the Second International Conference on Genetic Algorithms.1987． 82-89
Holland,J.H.,1975． Adaptation in Natural and Artificial Systems.The University of Michigan Press.
Hsu,Pai-Hui,Tseng Yi-Hsing,1999． Feature Extraction for Hyper Spectral Image.Asian Conference On Remote Sensing,November 22-25,1999,Hong Kong,China
Hsu,S.,1978． Texture tone analysis for automated land use mapping.Photogrammetry Engineering & Remote Sensing,vol.44(11) :1393-1404．
Huete,A.R.(1988) ,A soil adjusted vegetation index (SAVI),Remote Sens.Environ.,25:295-309．
Huguenin,R.L.,and Jones J.L.,1986． Intelligent information extraction from reflectance spectra: absorption band position.Journal of Geophysical Research,1986,91:pp.9585-9598．
Hush,D.R.,Home B.G,1993． Progress in supervised neural networks.IEEE Signal Processing Magazine,vol.10(1) :8-39．
Hwang,H.,Haddad R.A.,1995． "Adaptive median filters:new algorithms and results," IEEE Trans.Image Processing,4(4) :pp.499~502．
Irons,J.R.and Petersen,G.W.,1981． Texture transforms of remote sensing data.Remote Sens,of Environ.,11:359-370．
Jackson,R.D.,P.N.Slater,and P.J.Pinter,1983． Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres.Remote Sensing of the Environment,15:187-208．
Jensen,J.R.,1996,Introductory Digital Image Processing:A Remote Sensing Perspective London: Prentice Hall.
Jia,Xiuping and Richards,J.A.,1999． Segmented principal components transformation for
efficient hyperspectral remotesensing image display and classification.IEEE Trans.On Geosci.And Remote Sensing,vol.37(1) :538-542．
John,G,Kohavi,R.,and Pfleger,K.,1994． Irrelevant features and the subset selection problem.In Proceedings of the Eleventh International Conference on Machine Learning,pp.121-129, New Brunswick,NJ.Morgan Kaufmann.
Jordan,C.F.(1969) ,Derivation of leaf area index from quality of light on the forest floor,Ecology, 50:663-666．
Justusson,B.I.,1981． "Median filtering:statistics properties," in Two-Dimensional Digital Signal Processing,II:Transforms and Median Filters,Vol.42,New York:Springer Verlag,pp.161-196．
Kailath,T,1967,The divergence and Bhattacharyya distance measures in signal selection.IEEE T ransactions on Communications T echnology,COM-15,52-60．
Kaufman,Y.J.and Tanre,D.(1992) ,Atmospherically resistant vegetation index (ARVI)for EOS-MODIS,IEEE Trans.Geosci.Remote Sensing,30:261-270．
Kauth,R.J.and Thomas,G,1976． The tasseled cap-a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat,Proceedings of the Symposium on Machine Processing of Remotely Sensed Data,1976,Purdue Universtiy,West Lafayette,IN,pp.4 B-41-4 B-51．
Kim,S.R.,Efron A.,1995． "Adaptive robust impulse noise filtering," IEEE Trans.Signal Processing,43(8) :pp.1855-1866．
Kim,V,Yaroslavskii L.,1986． Rank algorithms for picture processing.Computer Vision,Graphics, Image Processing.35(2) :pp.234-258
Kira,K.and Rendell,L.,1992． A practical approach to feature selection.In Proceedings of the Ninth International Conference on Machine Learning,pp.249-256． Morgan Kaufmann.
Ko,S.J.,Lee Y.H.,1991． Center weighted median filters and their applications to image enhancement.IEEE Trans.Circuits Syst.38(9) :pp.983-993．
Kononenko,R.,and Agyepong,K.,1996． On lateral connections in feed-forward neural networks. In Proceedings of the International Conference on Neural Networks,pp.13-18．
Lampinen,J.,and Oja,E.,1995,Distortion tolerant pattern recognition based on selforganizing feature extraction.IEEE Transactions on Neural Networks,6,539-547．
Landgrebe,D.,1998． Information extraction principles and methods got multispectral and hyperspectral image data,in C.H.Chen (editor),Information Processing for Remote Sensing. Rover Edge,NJ:World Scientific Publishing.
Langley,P.,1994． Selection of relevant features in machine learning.In Proceedings of the AAAI Fall Symposium on Relevance,pp.1-5,New Orleans,LA.AAAI Press.
Lee,C.and Landgrebe,D.A.,1991． Fast likelihood classification.IEEE Trans.Geosci.& Remote Sensing,vol.29(4) :509-517
Lee,C.,and Landgrebe,D.A.1993b,Decision boundary feature extraction for non-parametric classifiers.IEEE Transactions on Systems,Man and Cybernetics,23,433-444．
Lee,C.,and Landgrebe,D.A.,1992,Decision boundary feature selection for neural networks. Proceedings of the I.E.E.E.International Conference on Systems,Man and Cybernetics (New York:I.E.E.E.Press),pp.1053-1057．
Lee,C.,and Landgrebe,D.A.,1993a,Feature extraction and classi(?) cation algorithms for high dimensional data.Technical Report TR-EE 93-1,School of Electrical Engineering,Purdue
University,West Lafayette,Indiana,U.S.A.
Lee,C.,and Landgrebe,D.A.,1993c,Feature extraction based on decision boundaries.IEEE Transactions on Pattern Analysis and Machine Intelligence,15,388-400．
Lee,J.B.,Woodyatt,A.S.and Herman,M.,1990． Enhancement of high spectral resolution remote sensing data by noise-adjusted principal components transform.IEEE Transaction on Geoscience and Remote Sensing,28,295-304．
Li,J.,Bruce,L.M.and Barnett,J.,2001． Automated detection of Pueraria Montana(kudzu) through Haar analysis of hyperspectral reflectance data.Proceedings of IGARSS, pp.2247-2249,July 2001．
Li,X.,Strahler.A.H.,1985． Geometric-Optical Modeling of a Conifer Forest Canopy.IEEE Transactions on Geoscience and Remote Sensing.46(12) :1563-1573
Liang,S.and Strahler A.,1993． An analytic BRDF model of canopy radiative transfer and its inversion.IEEE Transaction on Geoscience and Remote Sensing,31(5) :1081-1092．
Lin,H.M.,Willson A.N.,1988． Median Fiters with adaptive length.IEEE CAS1,35(6) :pp675-690
Lin,J.H.,Sellke T.M.,Coyle E.J.,1990． Adaptive stack filtering under the mean absolute error criterion.IEEE Trans.Acoust.Speech Signal Processing,vol.38(6) :pp.938-954．
Liu,H.Q.,and Huete,A.R.(1995) ,A feedback based modification of the NDVI to minimize canopy background and atmospheric noise,IEEE Trans.Geosci.Remote Sensing, 33:457-465．
Liu,H.,and Setiono,R.,1996a.Feature selection and classification-a probabilistic wrapper approach.In Proceedings of the Ninth International Conference on Industrial and Engineering Applications of AI and ES.
Liu,H.,and Setiono,R.,1996b.A probabilistic approach to feature selection-a filter solution.In Proceedings of the Thirteenth International Conference on Machine Learning.Morgan Kaufmann.
Lloyd,D.,1990． A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery.Int.J.Remote Sensing,1990 vol.11(12) :2269-2279
Loveland,T.R.,et al,1991． Development of a Land-Cover Characteristics Database for the Conterminous U.S..Photogrammetry Engineering & Remote Sensing,vol 57(11) : pp.1453-1463
Mandelbrot,B.B.,1967． How long is the coast of Britain? Statistical self-similarity and fractional dimension,Science 156,636-638
Mandelbrot,B.B.,1977． Fractals:Form,Chance and Dimension.San Francisco,CA:Freeman.
Mao,J.,and Jain,A.K.,1995,Artificial neural networks for feature extraction and multivariate data projection.I.E.E.E.Transactions on Neural Networks,6,296-317．
Mather,P.M.,1999,Computer Processing of Remotely-Sensed Images:An Introduction,2nd edn (Chichester,UK:John Wiley).
Mausel,P.W.,Kramber,W.J.,and Lee,J.K.,1990,Optimum band selection for supervised classi. cation of multispectral data.Photogrammetric Engineering and Remote Sensing,56,55-60．
Mayaux,P.and Lambin,E.F.,1997． Tropical forest area measured from global land-cover classifications:Inverse calibration models based on spatial textures.Remote Sens.Environ., 59:29-43．
McGillem,C.D.,Anuta,P.E.,Malalaret,E.and Yu,K.B.,1983． Estimation of a remote sensing
system point spread function from measured imagery[A].Proc.Symp.Machine Processing of Remotely Sensed Data[C],Purdue University,Lafayette,Ind.,1983,pp.62-68．
Michalewicz Z.,1992． Genetic Algorithms + Data Structures=Evolution Programs. Springer-Verlag,New York.
Miranda,F.and MacDonald,J.,1992． Application of the semivariogram textural classifier(STC) for vegetation discrimination using SIR-B data of Borneo.Int.J.Remote Sensing,vol.13:2349-2354
Motwani,R.and Raghavan,P.,1996． Randomized algorithms.ACM Computing Surveys,28(1) :33-37．
Narendra,P.,and Fukunaga,K.,1977． A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers,26:917-922．
Neilsen,A.A.,1994． Analysis of regularly and irregularly sampled spatial,multivariate,and multi-temporal data.Ph.D.thesis,Institute of Mathematical Modeling,Technical University of Denmark,Lyngsby,Denmark.
Neilsen,A.A.,Conradsen,k.and Simpson,J.J.,1998． Multivariate alteration detection(MAD) and MAP post-processing in multispectral,bi-temporal image data:new approaches to change detection studies,Remote Sensing of Environment,64:1-19．
Nilsson N.J.,1998． Artificial Intelligence:A New Synthesis.Morgan Kaufmann Publishers,Inc: San Francisco,CA.
Oja,E.,1995,PCA,ICA,and non-linear Hebbian learning.Proceedings of the International Conference on Artificial Neural Networks (ICANN '95) ,Paris,France,9-13 October 1995, pp.89-94．
Olsen,E.R.,R.D.Ramsey,and D.S..Winn.1993． A Modified Fractal Dimension as a Measure of Landscape Diversity.Photogrammetric Engineering & Remote Sensing.Vol. 59(10) :1517-1520
Pal S.K.,Bandyopadhyay S.,Murthy C.A.,2001． Genetic classifier for remotely sensed images: comparison with standard methods.Int.J.Remote Sensing.Vol.22(13) :pp2545-2569
Perkins S.,Theiler J.,Brumby S.P.,Harvey N.R.,Porter R.B.,Szymanski J.J.,and Bloch J.J., GENIE-A Hybrid Genetic Algorithm for Feature Classification in Multi-Spectral Images, 2000,Proc.SPIE 4120．
Philpot,W.D.,1991． The Derivative Ratio Algorithm:Avoiding Atmospheric Effects in Remote Sensing",IEEE Transactions on Geoscience &Remote Sensing,29(3) :350 357．
Punch,W.,Goodman,E.,Pei,M.,Chia-Shun,L.,Hovland,P.,and Enbody,R.,1993． Further research on feature selection and classification using genetic algorithms.In Proceedings of the International Conference on Genetic Algorithms,pp.557-564． Springer.
Quinlan,R.,1993． C4． 5:Programs for Machine Learning.Morgan Kaufmann,San Mateo,CA.Rao,C.R.,1965． Linear Statistical Inference and Its Applications,John Wiley and Sons,New York,1965．
Richards,J.A.,1993． Remote Sensing Digital Image Analysis:An introduction,Springer-Verlag Berlin Heidelberg,Second Edition.
Richardson,A.J.and Wiegand,C.L.(1977) ,Distinguishing vegetation from soil background information,Photogramm.Eng.Remote Sens.,43:1541-1552．
Richeldi,M.,and Lanzi,P.,1996． Performing effective feature selection by investigating the deep structure of the data.In Proceedings of the Second International Conference on Knowledge
Discovery and Data Mining,pp.379-383． AAAI Press.
Ripley,B.,1996． Pattern Recognition and Neural Networks.Cambridge University Press,New York.
Rissanen,J.,1978． Modelling by shortest data description.Automatica,14:465-471．
Ruiz,C.P.and Lopez F.J.A,2002． Restoring SPOT images using PSF-derived deconvolution filters[J].Int.J.Remote Sensing,23(12) :2379-2391．
Running S.W.,et al,1995． A Remote Sensing Based Vegetation Classification Logic for Global Land Cover Analysis[J].Remote Sens,of Environ.,vol 51:pp.39-48
Russell,S.,and Norvig,P.,1995． Artificial Intelligence:A Modern Approach.Prentice Hall, Englewood Cliffs,NJ.
Ryherd,S.and Woodcock,C.,1996． Combining spectral and textural data in the segmentation of remotely sensed images.Photogrammetry Engineering & Remote Sensing, vol.62(2) :181-194．
Sali,E.and Wolfson,H.,1992． Texture classification in aerial photographs and satellite data.Int.J. Remote Sensing,vol.13:3395-3408
Schowengerdt,R.A..Remote Sensing:Models and Methods for Image Processing (Second Edition).San Diego:Academic Press,1997．
Sellers,P.J.,Dickinson,R.E.,Randall,D.A.,Betts,A.K.,Hall,F.G,Mooney,H.A.,Nobre,C. A.,Sato,N.,Field,C.B.,and Henderson-Sellers,A.,1997． Modeling the exchanges of energy,water,and carbon between continents and atmosphere.Science,275:pp.502-509
Sheinvald,J.,Dom,B.,and Niblack,W.,1990． A modeling approach to feature selection.In Proceedings of the Tenth International Conference on Pattern Recognition,pp.535-539．
Shi Z.,Zhang D.S.,Kouri D.J.,and Hoffman D.K..Nonlinear Quincunx Filters.IEEE Transaction on PAMI (submitted).
Siedlecki,W.and Sklansky,J.,1988． On automatic feature selection.International Journal of Pattern Recognition,2:197-220．
Siedlecki,W.,and Sklansky,J.,1989． A note on genetic algorithms for large-scale feature selection. IEEE Transactions on Computers,10:pp.335-347．
Skalak,D.,1994． Prototype and feature selection by sampling and random mutation hill-climbing algorithms.In Proceedings of the Eleventh International Conference on Machine Learning, pp.293-301,New Brunswick,NJ.Morgan Kaufmann.
Story,M.and Congalton,R.(1986) .Accuracy assessment:a user's perspective.Photogramm.Eng. Remote Sens.vol.52(3) :pp397-399．
Sun,T.,Gabbouj M.,and Neuvo Y.,1992． Deterministic properties of center weighted median filters.Proc.1992 IEEE Int.Conf.Commun.Technol.Tsinghua Campus,Beijing,China.
Swain,P.H.and Davis,S.M.(editors),1978． Remote Sensing:The Quantitative Approach.New York:McGrowHill.
Switzer,P.and Green,A.A.,1984． Min/Max Autocorrelation Factors for Multivariate Spatial Imagery,Tech.Report No.6,Department of Statistics,Stanford University,Stanford,CA.
Tadjudin,S.and D.Landgrebe,1998． "Classification of High Dimensional Data with Limited Training Samples",PhD Thesis and School of Electrical & Computer Engineering Technical Report TR-ECE 98-8．
techniques.International Journal of Remote Sensing,vol.17:2215-2242．
Theiler J.,Harvey N.R.,Brumby S.P.,Szymanski J.J.,Alferink S.,Perkins S.,Porter R.,and
Bloch J.J.,"Evolving Retrieval Algorithms with a Genetic Programming Scheme",1999, Proc.SPIE 3753．
Thomas,I.L.,Benning,V.M.,and Ching,N.P.,1987a,Classi.cation of Remotely Sensed Images London:Adam Hilger.
Thomas,I.L.,Ching,N.P.,Benning,V.M.,and D'Aguanno,J.A.,1987b,A review of multi-channel indices of class separability.International Journal of Remote Sensing,8, 331-350．
Townshend,J.,Justice C.,Li W.,Gurney C.,McManus J.,1991． Global Land Cover Classification by Remote Sensing:Present Capabilities and Future Possibilities.Remote Sens,of Environ., vol 35:pp.243-255．
Townshend,J.R.G,Justice,C.O.,Kalb,V.,Characterization and classification of South American land cover types using satellite data.International Journal of Remote Sensing, 1987,7:1395-1416．
Tso,B.and Mather,P.M.,2001． Classification methods for remotely sensed data.Taylor-Francis Inc.New York,NY,USA.
Tso,B.,and Mather,P.M.,1999,Crop discrimination using multi-temporal SAR imagery. International Journal of Remote Sensing,20,2443-2460．
Tucker,C.J.,Townshend,J.R.G and Goff,T.E.1984． Continental land cover classification using meteorological satellite data.Science,vol.227:pp.369-375．
Tucker,C.J.,W.W.Newcomb,S.O.Los,and S.D.Prince,1991． Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989． International Journal of Remote Sensing,12:1113-1115．
Tucker,C.J.1979． Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment,8:127-150．
Tukey,J.W.,Exploratory data analysis.Addison-Wesley,New York,1971．
Unser,M.and Eden,M.,1989． Multiresolution feature extraction and selection for texture segementation.IEEE Trans.P.A.M.I.,vol.11(7) :717-728．
Vafaie,H.,and De Jong,K.,1993． Robust feature selection algorithms.In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence,pp.256-363．
Wang,Z..,Zhang D.,1998． "Restoration of impulse noise corrupted images using long-range correlation," IEEE Signal Processing Letter,Vol.5:pp.4-7．
Wang,F.,1990． Fuzzy supervised classification of remote sensing images.IEEE Trans.Geosci.& Remote Sensing,vol.28(2) :194-201．
Wickerhauser,V.,1991． INRIA lectures on wavelet packet algorithms.
Wendt,P.D.,Coyle E.J.,and Gallagher N.C.,1986． Stack filters.IEEE Trans.Acoust.Speech Signal Processing,vol.34(8) :pp.898-911．
Woodcock,C.E.and Strahler A.H.,1983． Characterising spatial patterns in remotely sensed data. Proceedings of the 17th International Symposium on Remote Sensing of Environment.Ann Arbor,MI:University of Michigan,pp.839-852．
Yang,J.,Parekh,R.,and Honavar,V.,1998． DistAI:An Inter-pattern Distance-based Constructive Learning Algorithm.Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (UJCNN'98) ,Anchorage,AK.May 4-8,1998． pp.2208-2213．
Yao,X.,1999． Evolving artificial neural networks.Proceedings of the IEEE,vol.87(9) ,pp. 1423-1447．
Yli-Harja O.,Stola J.,and Neuvo Y.,1991． Analysis of the properties of median and weighted median filters using threshold logic and stack filter representation.IEEE Trans.Signal Processing,vol.39(2) :pp.395-410．
You,X.,Grebbin G,1995． A robust adaptive estimator for filtering noise in images,IEEE Trans. Image Processing,4(5) :pp.693~699．
Young,T.Y.and Fu K.S.,1986． Handbook of Pattern Recognitions and Image Processing,College of Engineering,University of Miami,.Coral Gables,Florida.
Zenzo,S.D.,et al,1987a.Gaussian maximum likelihood and contextual classification algorithm for multicrop classification.IEEE Trans.Geosci.& Remote Sensing,GE-25(6) :805-814．
Zenzo,S.D.,et al,1987b.Gaussian maximum likelihood and contextual classification algorithm for multicrop classification experiments using Thematic Mapper and Multispectral Scanner Sensor data.IEEE Trans.Geosci.& Remote Sensing,GE-25(6) :815-824．
Zhang,D.,Wang Z.,1997． Impulse noise detection and removal using fuzzy techniques. Electronics letters,33(5) :pp.378-379