高分辨率遥感影像变化检测的关键技术研究
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摘要
高分辨率遥感影像包含更多、更丰富的地理和地形信息,已经广泛应用到社会经济的很多领域。尽管如此,高分辨率遥感影像在变化检测中仍面临很多问题：地物内部的异质性、丰富的纹理；阴影及投影差；混合像元问题等。其中,几何配准误差、相对辐射校正精度、阴影、像元尺度上较为破碎的变化检测结果、噪声等最为关键,本论文从这几个问题出发,针对变化检测中的变化与未变化两种类别对象,对高分辨率遥感影像变化检测的关键技术进行研究,主要包括以下几个方面：
(1)提出了一种基于多变量变化检测(MAD)得到的差异影像为基础,做面向对象后分类处理的OB-MAD方法,减小了高分辨率遥感影像变化检测中不可避免的几何配准误差和阴影等造成的“伪变化信息”。OB-MAD方法结合了多变量变化检测(MAD)和基于对象的后分类方法。一方面,充分利用了面向对象和面向像元两种方法的特点,提高了变化检测精度。另一方面,几何配准误差和阴影等在高分辨率的遥感影像中具有较好的形状特征,MAD变换得到的差异影像集中了所有的变化信息,为面向对象的后分类处理,剔除“伪变化信息”奠定基础。航拍影像通常具有1m以内的空间分辨率,其几何校正、阴影和噪声等问题比卫星影像更为明显,是多时相航拍影像变化检测的一个瓶颈。湿地生态系统又是全球环境变化检测中的一个重要领域,湿地植被的变化、阴影等问题是湿地变化检测研究的重点和难点。因此,选用2006和2007年5月的航拍影像(分辨率0.15m)对美国加州中部的Gadwall North湿地生态系统进行变化检测研究,详细比较了基于差异影的OB-MAD和其他几种方法(OB-traditional, Threshold-MAD和PB-MAD)在变化检测中的差异。研究结果表明,针对变化检测研究中几何配准等造成的“伪变化信息”,三种基于MAD差异影像的方法取得的精度较高。其中,OB-MAD方法得到的总体精度最高(93.54%),其次是Threshold-MAD (90.07%)和PB-MAD (86.09%),相应的地面未变化像元的用户精度也是OB-MAD最高(90.57%),PB-MAD (82.2%)和Threshold-MAD (81.49%)。
(2)多时相高分辨率遥感影像的变化检测预处理过程中,不可避免的几何配准误差、混合像元等都会带来一定的影响。针对这些问题,提出将广泛用于纹理特征提取、滤波除噪等方面的窗口用于相对辐射校正,用窗口区域范围内计算的相对平稳的值作为该区域的特征提高相对辐射校正的精度。研究选择广东番禺区两处具有不同空间异质性的区域,选择2005和2006的SPOT5影像(分辨率2.5m)和2006及2007的航拍影像(分辨率0.15m),3×3,5×5,7×7像元大小的窗口,自动的相对辐射校正算法MAD和稳健回归,进行基于窗口的相对辐射校正方法的敏感性分析。研究结果表明窗口的应用较好的提高了相对辐射校正的精度,同时1)不同空间异质性的遥感影像获得最佳辐射校正所需的窗口大小不一：异质性影像选择3×3像元,而均质性影像则选择5×5像元；2)local size尺度上,稳健回归在不同异质性的遥感影像中差异显著：异质性影像中稳健回归优于传统的手动方法,而在均质性影像中则表现的没有传统方法稳定,而MAD方法在两种空间特性的影像中均表现出比稳健回归更强的鲁棒性；3)几何配准误差在具有较高空间分辨率的航拍影像的变化检测中更为明显,提高相对辐射校正所需窗口较大。
(3)基于波段相减的图像差值法对影像的质量和预处理要求相对较高,通常只用于来自同一传感器、同一时相的遥感数据；预处理中不可避免的几何配准误差、相对辐射校正精度、阴影等都是基于差异影像的高分辨率遥感影像变化检测中的典型问题；像素尺度上得到的变化检测结果较为破碎。在这样的背景下,提出一种面向对象的高分辨率差异图像的变化检测方法(OB-EM),即在对象尺度上,对基于MAD变换和最小噪声比率变换(MNF)得到的差异影像做阈值分析,将像元—像元之间的差异影像推广到对象—对象。研究选择广东番禺区2005和2006的SPOT5影像(分辨率2.5m),分析比较对象的不同特征选择、不同尺度的差异影像(对象和像元)、不同的方法(DFPS、Gams、EM、OB-MAD)等对变化检测的影响。研究结果表明：1)基于决策树的特征选择极大的提高了面向对象的变化检测精度；2)提出的OB-EM方法使得来自不同传感器的遥感影像用于变化检测变的可能；减小了对原始数据的要求和多时相影像的相对辐射校正的要求；改善了“椒盐效应”,提高了高分辨率遥感影像的变化检测精度。3)基于Kappa系数的显著性分析表明OB-EM和OB-MAD两者之间差异显著,说明基于对象的差异影像(OB-EM)比基于像元的差异影像(OB-MAD)能更好的减小甚至避免多时相高分辨率遥感影像变化检测中不可避免的几何配准误差和阴影引起的“伪变化信息”,具有较强的鲁棒性。
High resolution imagery can provide more execute detail information, and have been widely used in scientific application. However, due to heterogeneity, texture, shadow and mixed pixel problem, there are big challenges in change detection using high resolution remotely sensed imageries. Additionally, spatial mis-registration, shadow and noise are common drawbacks of pixel-based method and particularly conspicuous in high resolution imagery, which can exaggerate or make thematic changes in change detection analysis. Considerable effort had been expended on minimizing the influence of these problems. In this study, we focused on these big issues and did the research on the key technology of change detection using high resolution remotely sensed imagery.
(1) With respect to the inevitable mis-registration and shadow effects on change detection analysis, we proposed an object-based post-classification of the Multivariate Alteration Detection components method (OB-MAD), which can take advantage of pixel and object-based method. The extended difference image generated from MAD components can enhance the change information as well as amplyfying the false change caused by geometric distortion and shadow to an extent. On basis of the good shape information of false change, we proposed to do the object-based post-processing on this extended difference image. Obviously, aerial photograph with a high resolution less than 1 meter were useful in change detection, which can provide the real time multi-temporal imageries. However, compared with widely usd satellite imageries, the geometric distortion, shadow and noise problems of aerial imagery were significant. Change detection on wetland is a big topic in environmental monitoring and it is very difficult to detect the changes of vegetation and exclude the false information. Thus, very high spatial resolution images of drained managed wetland ponds (Gadwell North) were used to compare the proposed OB-MAD method with three commonly used classification methods in terms of minimizing the influence of mis-registration and shadow on the change detection analysis:(1) the traditional MAD method with thresholds (Threshold-MAD), (2) a pixel-based post-classification of MAD components with decision tree analysis (PB-MAD), and a traditional object-based post-classification method (OB-traditional). The proposed OB-MAD method, which utilized shape and textural information of objects derived from MAD components, produced the highest accuracy with respect to wetland change detection and successfully minimized the influence from the geometric distortion and shadow on the changed area. Overall accuracy was best for the OB-MAD method (93.54%), followed by the Threshold-MAD (90.07%), and PB-MAD (86.09%). The OB-MAD method also resulted in the greatest user's accuracy for no-change pixels (90.57%), compared to 82.2% and 81.49% for PB-MAD and Threshold-MAD, respectively.
(2) Inevitable geometric distortion of multi-temporal imageries and mixed pixel can significantly influence the accuracy of pre-processing in change detection analysis. New pixel represented by the averaged features within the window size can provide more reliable information, which had been widely used in texture extraction and filtering, since we proposed to combine the window size into the automatic relative radiometric correction methods. In our study, we tried to find out whether the proposed window size based relative radiometric correction method can improve accuracy. In addition, due to the fact that the accuracy of relative radiometric correction is different at homogeneous and heterogeneous landscape, we tried to analyze the sensitivity of window size at different landscapes. Homogeneous and heterogeneous landscapes generated from SPOT-5 imageries of 2005 and 2006 of PanYu city, China was preferred as our study areas. Three window size with 3 x 3,5×5 and 7 x 7 pixel, and two relative radiometric correction methods named MAD and robust regression were used to compare with the traditional manual method using two spatial resolution imageries of aerial image and SPOT satellite iamge. All of the results indicated that the optimun window size of proposed relative radiometric correction was significantly different at homogeneous and heterogenous landscapes. Specifically, for the homogeneous landscape, the optimun window size was 3 x 3 pixel, while 5×5 pixel window size was selected at heterogeneous landscape. Moreover, compared with traditional manual method at local size, robust regression was significantly different at both landscapes, while MAD method got robust better results. The proposed window size based relative radiometric correction method was sensitive to the spatial resolution of images.
(3) Traditional pixel-based change detection method using difference image is difficult to perform change on multi-sensor imageries, because of the different number of bands and the impact of inter-channel correlation. In addition, pixel-based change detection methods are more sensitive to the inevitable geometric distortion, shadow and noise in pre-processing for high resolution imageries, and often suffer from "salt and pepper" effect in the resulting map. Note that another bottleneck is how to reasonably determine the threshold of change and no-change of difference image. In this context, we proposed an object-based method based on difference image generated from objects (OB-EM). Firstly, we got the object-based difference image using MAD and MNF method, and then found the change and no-change information using EM method. The proposed OB-EM method can take advantage of salient aspects of the MAD, MNF and object-based methods. SPOT-5 imagery of 2005 and 2006 in a case study of PanYu city, China were used to validate the proposed OB-EM method by comparing with other methods (DFPS, Gams, OB-traditional and OB-MAD). All the results indicated that feature selection can significantly improve the accuracy of change detection. Second, the proposed OB-EM method can take advantage of MAD, MNF and EM, which could deal with the data getting from different sensors. The "salt-and-pepper" effect could be improved well. Moreover, the accuracy of pre-processing of change detection was not required as high as OB-MAD. Finally, Z-test got a significant result between OB-EM and OB-MAD. It demonstrated that OB-EM was such a better method that can obviously recognize the false change from mis-registration and shadow.
(1)提出了一种基于多变量变化检测(MAD)得到的差异影像为基础,做面向对象后分类处理的OB-MAD方法,减小了高分辨率遥感影像变化检测中不可避免的几何配准误差和阴影等造成的“伪变化信息”。OB-MAD方法结合了多变量变化检测(MAD)和基于对象的后分类方法。一方面,充分利用了面向对象和面向像元两种方法的特点,提高了变化检测精度。另一方面,几何配准误差和阴影等在高分辨率的遥感影像中具有较好的形状特征,MAD变换得到的差异影像集中了所有的变化信息,为面向对象的后分类处理,剔除“伪变化信息”奠定基础。航拍影像通常具有1m以内的空间分辨率,其几何校正、阴影和噪声等问题比卫星影像更为明显,是多时相航拍影像变化检测的一个瓶颈。湿地生态系统又是全球环境变化检测中的一个重要领域,湿地植被的变化、阴影等问题是湿地变化检测研究的重点和难点。因此,选用2006和2007年5月的航拍影像(分辨率0.15m)对美国加州中部的Gadwall North湿地生态系统进行变化检测研究,详细比较了基于差异影的OB-MAD和其他几种方法(OB-traditional, Threshold-MAD和PB-MAD)在变化检测中的差异。研究结果表明,针对变化检测研究中几何配准等造成的“伪变化信息”,三种基于MAD差异影像的方法取得的精度较高。其中,OB-MAD方法得到的总体精度最高(93.54%),其次是Threshold-MAD (90.07%)和PB-MAD (86.09%),相应的地面未变化像元的用户精度也是OB-MAD最高(90.57%),PB-MAD (82.2%)和Threshold-MAD (81.49%)。
(2)多时相高分辨率遥感影像的变化检测预处理过程中,不可避免的几何配准误差、混合像元等都会带来一定的影响。针对这些问题,提出将广泛用于纹理特征提取、滤波除噪等方面的窗口用于相对辐射校正,用窗口区域范围内计算的相对平稳的值作为该区域的特征提高相对辐射校正的精度。研究选择广东番禺区两处具有不同空间异质性的区域,选择2005和2006的SPOT5影像(分辨率2.5m)和2006及2007的航拍影像(分辨率0.15m),3×3,5×5,7×7像元大小的窗口,自动的相对辐射校正算法MAD和稳健回归,进行基于窗口的相对辐射校正方法的敏感性分析。研究结果表明窗口的应用较好的提高了相对辐射校正的精度,同时1)不同空间异质性的遥感影像获得最佳辐射校正所需的窗口大小不一：异质性影像选择3×3像元,而均质性影像则选择5×5像元；2)local size尺度上,稳健回归在不同异质性的遥感影像中差异显著：异质性影像中稳健回归优于传统的手动方法,而在均质性影像中则表现的没有传统方法稳定,而MAD方法在两种空间特性的影像中均表现出比稳健回归更强的鲁棒性；3)几何配准误差在具有较高空间分辨率的航拍影像的变化检测中更为明显,提高相对辐射校正所需窗口较大。
(3)基于波段相减的图像差值法对影像的质量和预处理要求相对较高,通常只用于来自同一传感器、同一时相的遥感数据；预处理中不可避免的几何配准误差、相对辐射校正精度、阴影等都是基于差异影像的高分辨率遥感影像变化检测中的典型问题；像素尺度上得到的变化检测结果较为破碎。在这样的背景下,提出一种面向对象的高分辨率差异图像的变化检测方法(OB-EM),即在对象尺度上,对基于MAD变换和最小噪声比率变换(MNF)得到的差异影像做阈值分析,将像元—像元之间的差异影像推广到对象—对象。研究选择广东番禺区2005和2006的SPOT5影像(分辨率2.5m),分析比较对象的不同特征选择、不同尺度的差异影像(对象和像元)、不同的方法(DFPS、Gams、EM、OB-MAD)等对变化检测的影响。研究结果表明：1)基于决策树的特征选择极大的提高了面向对象的变化检测精度；2)提出的OB-EM方法使得来自不同传感器的遥感影像用于变化检测变的可能；减小了对原始数据的要求和多时相影像的相对辐射校正的要求；改善了“椒盐效应”,提高了高分辨率遥感影像的变化检测精度。3)基于Kappa系数的显著性分析表明OB-EM和OB-MAD两者之间差异显著,说明基于对象的差异影像(OB-EM)比基于像元的差异影像(OB-MAD)能更好的减小甚至避免多时相高分辨率遥感影像变化检测中不可避免的几何配准误差和阴影引起的“伪变化信息”,具有较强的鲁棒性。
High resolution imagery can provide more execute detail information, and have been widely used in scientific application. However, due to heterogeneity, texture, shadow and mixed pixel problem, there are big challenges in change detection using high resolution remotely sensed imageries. Additionally, spatial mis-registration, shadow and noise are common drawbacks of pixel-based method and particularly conspicuous in high resolution imagery, which can exaggerate or make thematic changes in change detection analysis. Considerable effort had been expended on minimizing the influence of these problems. In this study, we focused on these big issues and did the research on the key technology of change detection using high resolution remotely sensed imagery.
(1) With respect to the inevitable mis-registration and shadow effects on change detection analysis, we proposed an object-based post-classification of the Multivariate Alteration Detection components method (OB-MAD), which can take advantage of pixel and object-based method. The extended difference image generated from MAD components can enhance the change information as well as amplyfying the false change caused by geometric distortion and shadow to an extent. On basis of the good shape information of false change, we proposed to do the object-based post-processing on this extended difference image. Obviously, aerial photograph with a high resolution less than 1 meter were useful in change detection, which can provide the real time multi-temporal imageries. However, compared with widely usd satellite imageries, the geometric distortion, shadow and noise problems of aerial imagery were significant. Change detection on wetland is a big topic in environmental monitoring and it is very difficult to detect the changes of vegetation and exclude the false information. Thus, very high spatial resolution images of drained managed wetland ponds (Gadwell North) were used to compare the proposed OB-MAD method with three commonly used classification methods in terms of minimizing the influence of mis-registration and shadow on the change detection analysis:(1) the traditional MAD method with thresholds (Threshold-MAD), (2) a pixel-based post-classification of MAD components with decision tree analysis (PB-MAD), and a traditional object-based post-classification method (OB-traditional). The proposed OB-MAD method, which utilized shape and textural information of objects derived from MAD components, produced the highest accuracy with respect to wetland change detection and successfully minimized the influence from the geometric distortion and shadow on the changed area. Overall accuracy was best for the OB-MAD method (93.54%), followed by the Threshold-MAD (90.07%), and PB-MAD (86.09%). The OB-MAD method also resulted in the greatest user's accuracy for no-change pixels (90.57%), compared to 82.2% and 81.49% for PB-MAD and Threshold-MAD, respectively.
(2) Inevitable geometric distortion of multi-temporal imageries and mixed pixel can significantly influence the accuracy of pre-processing in change detection analysis. New pixel represented by the averaged features within the window size can provide more reliable information, which had been widely used in texture extraction and filtering, since we proposed to combine the window size into the automatic relative radiometric correction methods. In our study, we tried to find out whether the proposed window size based relative radiometric correction method can improve accuracy. In addition, due to the fact that the accuracy of relative radiometric correction is different at homogeneous and heterogeneous landscape, we tried to analyze the sensitivity of window size at different landscapes. Homogeneous and heterogeneous landscapes generated from SPOT-5 imageries of 2005 and 2006 of PanYu city, China was preferred as our study areas. Three window size with 3 x 3,5×5 and 7 x 7 pixel, and two relative radiometric correction methods named MAD and robust regression were used to compare with the traditional manual method using two spatial resolution imageries of aerial image and SPOT satellite iamge. All of the results indicated that the optimun window size of proposed relative radiometric correction was significantly different at homogeneous and heterogenous landscapes. Specifically, for the homogeneous landscape, the optimun window size was 3 x 3 pixel, while 5×5 pixel window size was selected at heterogeneous landscape. Moreover, compared with traditional manual method at local size, robust regression was significantly different at both landscapes, while MAD method got robust better results. The proposed window size based relative radiometric correction method was sensitive to the spatial resolution of images.
(3) Traditional pixel-based change detection method using difference image is difficult to perform change on multi-sensor imageries, because of the different number of bands and the impact of inter-channel correlation. In addition, pixel-based change detection methods are more sensitive to the inevitable geometric distortion, shadow and noise in pre-processing for high resolution imageries, and often suffer from "salt and pepper" effect in the resulting map. Note that another bottleneck is how to reasonably determine the threshold of change and no-change of difference image. In this context, we proposed an object-based method based on difference image generated from objects (OB-EM). Firstly, we got the object-based difference image using MAD and MNF method, and then found the change and no-change information using EM method. The proposed OB-EM method can take advantage of salient aspects of the MAD, MNF and object-based methods. SPOT-5 imagery of 2005 and 2006 in a case study of PanYu city, China were used to validate the proposed OB-EM method by comparing with other methods (DFPS, Gams, OB-traditional and OB-MAD). All the results indicated that feature selection can significantly improve the accuracy of change detection. Second, the proposed OB-EM method can take advantage of MAD, MNF and EM, which could deal with the data getting from different sensors. The "salt-and-pepper" effect could be improved well. Moreover, the accuracy of pre-processing of change detection was not required as high as OB-MAD. Finally, Z-test got a significant result between OB-EM and OB-MAD. It demonstrated that OB-EM was such a better method that can obviously recognize the false change from mis-registration and shadow.
引文
Anderson, T. W.,2003. An Introduction to Multivariate statistical analysis, their Edition, John wiley & son Inc. Hoboken, Mewyerssy.
Argialas, D. and A. Tzotsos,2006. Automatic extraction of physiographic features and alluvial fans in Nevada, USA, from digital elevation models and satellite imagery through multiresolution segmentation and object-oriented classification:1?
Baraldi, A. and F. Parmiggiani,1996. Single linkage region growing algorithms based on the vector degree of match. Ieee Transactions on Geoscience and Remote Sensing.34(1):137-148.
Blaschke, T.,2004. Object based image analysis for remote sensing, Elsevier.65: 2-16.
Blaschke, T.,2010. Object based image analysis for remote sensing, Elsevier.65: 2-16.
Brown, K. M., G. M. Foody and P. M. Atkinson,2007. Modelling geometric and misregistration error in airborne sensor data to enhance change detection. International Journal of Remote Sensing.28(12):2857-2879.
Bruzzone, L. and D. F. Prieto,2000. Automatic analysis of the difference image for unsupervised change detection, IEEE.38:1171-1182.
Bruzzone, L. and S. B. Serpico,1997. An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images. Ieee Transactions on Geoscience and Remote Sensing.35(4):858-867.
Byrne, T. G., A. H. Mccain and T. M. Kretchun,1980. Testing Chrysanthemums for Disease Resistance. California Agriculture.34(11-1):14-15.
Canty, M. J.,2006. Image analysis, classification and change detection in remote sensing:with algorithms for ENVI/IDL. New York, CRC Press.
Canty, M. J. and A. A. Nielsen,2006. Visualization and unsupervised classification of changes in multispectral satellite imagery. International Journal of Remote Sensing.27(18):3961-3975.
Carleera, A., O. Debeirb and E. Wolffa,2004. Comparison of very high spatial resolution satellite image segmentations, Citeseer.5238:533.
Carvalho, L. M. T., L. M. G. Fonseca, F. Murtagh and J. Clevers,2001. Digital change detection with the aid of multiresolution wavelet analysis. International Journal of Remote Sensing.22(18):3871-3876.
Chatelain, F., J. Y. Tourneret, J. Inglada and A. Ferrari,2007. Bivariate gamma distributions for image registration and change detection. IEEE Transaction on image processing.16(7):1796-1806.
Chen, H. M., P. K. Varshney and M. K. Arora,2003. Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing.41(11):2445-2454.
Cihlar, J., R. Latifovic, J. Chen, J. Beaubien, Z. Li and S. Magnussen,2000. Selecting representative high resolution sample images for land cover studies. Part 2: Application to estimating land cover composition. Remote Sensing of Environment. 72(2):127-138.
Congalton, R. G.,1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment.37(1):35-46.
Congalton, R. G. and K. Green,1998. Assessing the accuracy of remotely sensed data:principles and practices. New York, CRC Press.
Congalton, R. G., R. G. Oderwald and R. A. Mead,1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing.49:1671-1678.
Coppin, P., I. Jonckheere, K. Nackaerts, B. Muys and E. Lambin,2004. Digital change detection methods in ecosystem monitoring:a review. International Journal of Remote Sensing.25(9):1565-1596.
Coppin, P. R. and M. E. Bauer,1996. Digital change detection in forest ecosystems with remotely sensed data. Remote Sensing Reviews.13:207-234.
Dai, X. and S. Khorram,1997. Quantification of the impact of misregistration on the accuracy ofremotely sensed change detection.4.
Dai, X. and S. Khorram,1998. The effects of image misregistration on the accuracy of remotely sensed change detection, IEEE.36:1566-1577.
Dai, X. and S. Khorram,1998. The effects of image misregistration on the accuracy of remotelysensed change detection. IEEE Transactions on Geoscience and Remote Sensing.36(5):1566-1577.
Dempster, A. P., N. M. Laird and D. B. Rubin,1977. Maximum likelihood from incomplete data via the EM algorithm, JSTOR.39:1-38.
Geneletti, D. and B. G. H. Gorte,2003. A method for object-oriented land cover classification combining Landsat TM data and aerial photographs, Taylor & Francis. 24:1273-1286.
Gong, P.,1992. An efficient contextual classifier for land-use classification. Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium,. Houston, United States:555-557.
Gong, P., E. F. Ledrew and J. R. Miller,1992. Registration-noise reduction in difference images for change detection.13:773-779.
Green, K., D. Kempka and L. Lackey,1994. Using Remote-Sensing to Detect and Monitor Land-Cover and Land-Use Change. Photogrammetric Engineering and Remote Sensing.60(3):331-337.
Guo, Q., M. Kelly, P. Gong and D. Liu,2007. An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience & Remote Sensing.44(1):24-47.
Hotelling, H.,1936. Relations between two sets of variates, Biometrika Trust.28: 321-377.
Im, J., J. R. Jensen and J. A. Tullis,2008. Object-based change detection using correlation image analysis and image segmentation, Taylor & Francis, Inc. Bristol, PA, USA.29:399-423.
Johnson, D. W. and D. E. Todd,1998. Effects of harvesting intensity on forest productivity and soil carbon storage in a mixed oak forest. Management of Carbon Sequestration in Soil.351-363 457.
Kern, K., W. Sulzer and G. K. Lieb,2009. Detection of land-cover and land-use change in the Hohe Tauern National Park (Austria) using an object-based classification approach:first results. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX,. Berlin, Germany 7478:55.
Labovitz, M. L. and J. W. Marvin,1986. Precision in geodetic correction of TM data as a function of the number, spatial distribution, and success in matching of control points-A simulation. Remote sensing of environment.20:237-252.
Landgrebe, D.,1999. Information extraction principles and methods for multispectral and hyperspectral image data, Chapter.82:3?8.
Lu, D., P. Mausel, E. Brondizio and E. Moran,2004. Change detection techniques, Taylor & Francis.25:2365-2401.
Manandhar, R.,I. O. A. Odeh and T. Ancev,2009. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement. Remote sensing of environment.1 (3).
Marceat, D. J., P. J. Howarth and J. M. Dubois,1990. Evaluation of the grey-level Co-occurrence matrix method for land cover classification using SPOT imagery. IEEE Transaction on Geoscience and remote sensing.28(4):513-519.
Marceau, D. J. and G. J. Hay,1999. Remote sensing contributions to the scale issue, Ottawa:Canadian Aeronautics and Space Institute,[1975]-c2004.25:357-366.
Markham, B. and T. J. R,1981. Land cover classification accuracy as a function of sensor spatial resolution Proceedings 15th on remote sensing of environment.
Meola, J. and M. T. Eismann,2008. Image misregistration effects on hyperspectral change detection.6966:69660Y.
Molinier, M., J. Laaksonen and T. Hame,2007. Detecting man-made structure and changes in satellite imagery with a content-based information retrieval system built on self-organizing map. IEEE Transaction on Geoscience and remote sensing. 45(4):861-874.
Moon, T. K.,1996. The expectation-maximization algorithm, IEEE.13:47-60.
Morisette, J. T. and S. Khorram,2000. Accuracy assessment curves for satellite-based change detection. Photogrammetric Engineering and Remote Sensing. 66(7):875-880.
Mouat, D. A., G. G. Mahin and J. Lancaster,1993. Remote sensing techniques in the analysis of change detection. Geocarto International.8(2):39-50.
Nielsen, A. A., K. Conradsen and J. J. Simpson,1998. Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote sensing of environment.64(1): 1-19.
Niemeyer, I. and M. J. Canty,2001. Knowledge-based interpretation of satellite data by object-based and multi-scale image analysis in the context of nuclear verification. IGARSS 2001 IEEE International Geoscience and Remote Sensing Symposium,9-13 July 2001, Sydeny, Australia,.
Niemeyer, I. and M. J. Canty,2003. Pixel-based and object-oriented change detection analysis using high-resolution imagery. Proc.25th Symposium on Safeguards and Nuclear Material Managment,13-15 May 2003 (CD-ROM). Stockholm.
Qian, Y., K. Zhang and F. Qiu,2005. Spatial contextual noise removal for post classification smoothing of remotely sensed images. Proceedings of the 2005 ACM symposium on Applied computing, Santa Fe, New Mexico ACM.
Quinlan, R.,2006. Rulequest research data mining tools. [Online].Available: http://www.rulequest.com/.
Read, J., J. M. Ferris and T. Jaffre,2002. Foliar mineral content of Nothofagus species on ultramafic soils in New Caledonia and non-ultramafic soils in Papua New Guinea. Australian Journal of Botany:50(5):607-617.
Roy, D. P.,2000. The impact of misregistration upon composited wide field of view satellite data and implications for change detection, Institute of Electrical and Electronics Engineers, Inc,445 Hoes Ln, Piscataway, NJ,08854-1331, UK.38: 2017-2032.
Salvador, E., A. Cavallaro and T. Ebrahimi,2004. Cast shadow segmentation using invariant color features. Computer vision and image understanding.95: 238-259.
Sasagawa, Y.,2005. Integrated shadow removal based on photogrammetry and image analysis. International Journal of Remote Sensing.26:18.
Schiewe, J., L. Tufte and M. Ehlers,2001. Potential and problems of multi-scale segmentation methods in remote sensing. GIS—Zeitschrift fur Geoinformationssysteme.6:34-39.
Singh, A.,1989. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing.10(6):989-1004.
Soille, P. and M. Pesaresi,2002. Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Transactions on Geoscience and Remote Sensing.40(9):2042-2055.
Stow, D. A.,1999. Reducing the effects of misregistration on pixel-level change detection, Taylor and Francis Ltd.20:2477-2483.
Stow, D. A. and D. M. Chen,2002. Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover changes and misregistration. Remote sensing of environment.80(2):297-307.
Strahler, A. H., C. E. Woodcock and J. A. Smith,1986. On the nature of models in remote sensing. Remote sensing of environment.20(2):121-139.
Townshend, J. R. G., C. O. Justice, C. Gurney and J. McManus,1992. The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing.30(5):1054-1060.
Van de Voorde, T., W. De Genst and F. Canters,2007. Improving pixel-based VHR land-cover classifications of urban areas with post-classification techniques. Photogrammetric Engineering & Remote Sensing.73(9):1017-1027.
Verbyla, D. L. and S. H. Boles,2000. Bias in land cover change estimates due to misregistration, London:Taylor & Francis, c1980-.21:3553-3560.
Walter, V.,2004. Object-based classification of remote sensing data for change detection. Photogrammetry and Remote Sensing.58(3-4):225-238.
Wang, L., W. P. Sousa and P. Gong,2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing.25(24):5655-5668.
Watanabe, S., K. Miyajima and N. Mukawa,1998. Detecting changes of buildings from aerial images using shadow and shading model.14th International Conference on Pattern Recognition (ICPR'98),August 16-August 20, Brisbane, Australia Published by the IEEE Computer Society.
Weismiller, R. A., S. J. Kristof, D. K. Scholz, P. E. Anuta and S. A. Momin, 1977. Change Detection in Coastal Zone Environments. Photogrammetric Engineering and Remote Sensing.43(12):1533-1539.
Xu, D., J. Z. Liu, Z. K. Liu and X. O. Tang,2004. Indoor shadow detection for video segmentation.2004 Ieee International Conference on Multimedia and Exp (Icme), Vols 1-3.41-44 2242.
Yang, X. and C. P. Lo,2000. Relative radiometric normalization performance for change detection from multi-date satellite images, ASPRS AMERICAN SOCIETY FOR PHOTOGRAMMETRY AND.66:967-980.
Zhou, W. and A. Troy,2008. An object-oriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing.29(11):3119-3135.
Zhou, W., A. Troy and M. Grove,2008. A Comparison of Object-Based with Pixel-Based Land Cover Change Detection in the Baltimore Metropolitan Area using Multitemporal High Resolution Remote Sensing Data. IGARSS 2008 IEEE International Geoscience and Remote Sensing Symposium, July 6-11,2008| Boston, Massachusetts, U.S.A.
Zorich, V.,1949. Communication in the presence of noise, Springer:55-75.
王文杰,赵忠明和朱海青,2009.面向对象特征融合的高分辨率遥感图像变化检测方法.计算机应用研究.(8)：3149-3151.
王伟平,2007.基于聚类分析的遥感图像变化检测方法研究,国防科学技术大学.
王广运,1996.差分GPS定位技术与应用.北京,电子工业出版社.
史培军,宫鹏和李晓,2000.土地利用覆盖变化研究的方法与实践.北京,科学出版社.
田新光,张继贤和张永红,2007.面向对象的红树林信息提取.海洋测绘.27(2)：41-44.
余鹏,封举富和童行伟,2005.一种新的基于高斯混合模型的纹理图像分割方法.武汉大学学报：信息科学版.30(6)：514-517.
李生平,1997.建筑工程测量.武汉,武汉工业大学出版社.
李向军,2006.遥感土地利用变化检测方法探讨,中国科学院研究生院(遥感应用研究院).
汪求来,2008.面向对象遥感影像分类方法及其应用研究,南京林业大学.
明冬萍,王群和杨建宇,2008.遥感影像空间尺度特性与最佳空间分辨率选择.遥感学报.(4)：529-537.
姜涛,马国锐 和秦前清,2005.基于遥感影像的变化检测技术.计算机应用研究.22(2)：255-257.
胡进刚,张晓东,沈欣 和张婵,2006.一种面向对象的高分辨率影像道路提取方法.遥感技术与应用.21(3)：184-188.
徐丽华,2005.顾及投影差的遥感影像变化检测,武汉大学.
崔林丽,史军,唐娉 和高志强,2005.中国陆地净初级生产力的季节变化研究.地理科学进展.24(3)：8-16,F003.
曹宝,秦其明,马海建和邱云峰,2006.面向对象方法在Spot5遥感图像分类中的应用——以北京市海淀区为例.地理与地理信息科学.22(2)：46-49,54.
梁俊,周武,陈川和董玉森,2008.基于滑动窗口法的遥感图像纹理统计信息提取研究.安徽农业科学.36(15)：6426-6428.
梅安新和彭望琭,2002.遥感概论.北京,高等教育出版社.
盛辉,廖明生和张路,2004.基于典型相关分析的变化检测中变化阈值的确定.遥感学报.8(5)：452-457.
廖明生和朱攀,2000.基于典型相关分析的多元变化的检测.遥感学报.4(3)：197-201.
廖明生和林晖,2003.雷达干涉测量——原理与信号处理基础.北京,测绘出版社.
蔡运龙,2001.土地利用/土地覆被变化研究：寻求新的综合途径.地理究.(6)：645-652.
霍春雷,程健,卢汉清和周志鑫,2008.基于多尺度融合的对象级变化检测新方法.自动化学报.34(3)：251-257.
鞠何其,2009.基于色彩空间变换的遥感影像阴影检测与去除技术上海交通大学.
刘小洲,2007.高分辨率遥感图像的变化检测技术研究,国防科学技术大学.
刘建军,2008.苍山县矿产资源开发遥感动态监测与景观生态重建潜力研究,山东师范大学.
刘慧平和朱启疆,1999.应用高分辨率遥感数据进行土地利用与覆盖变化监测的方法及其研究进展.资源科学.21(3)：23-27.
华媛媛,2009.纹理信息在遥感图像分类中的应用于研究,西安科技大学.
吴剑,2010.基于面向对象技术的遥感震害信息提取与评价方法研究,武汉大学.
张振龙,曾志远,李硕和胡子付,2005.遥感变化检测方法研究综述.遥感信息.(5)：64-66,59.
张路,2004.基于多元统计分析的遥感影像变化检测方法研究,武汉大学.
张汉松,2010.基于对象的海岸带地物变化检测技术的研究,浙江大学.
许妙忠和余志惠,2003.高分辨率卫星影像中阴影的自动提取与处理.测绘信息与工程.28(1)：20-22.
赵英时,2003.遥感应用分析原理与方法.北京,科学出版社.
邓劲松,石媛媛,陈利苏,王珂和祝锦霞,2009.基于近红外传感器和面向对象光谱分割技术的田间棉株识别与提取.光谱学与光谱分析(7)：1754-1758.
钟家强,2005.基于多时相遥感图像的变化检测,国防科学技术大学.
钟家强和王润生,2006.基于独立成分分析的多时相遥感图像变化检测.电子与信息学报.28(6)：994-998.
陈江龙,曲福田 和王启仿,2003.经济发达地区土地利用结构变化预测——以江苏省江阴市为例.长江流域资源与环境.12(4)：317-321.
陈建和窦贻俭,1996.地理信息系统支持下的城市生态环境功能区划.城市环境与城市生态.9(1)：24-30.
陈述彭,1990.遥感地学分析.北京,测绘出版社.
陈国宏,蔡彬清和李美娟,2007.元胞自动机：一种探索管理系统复杂性的有效工具.中国工程科学.9(1)：28-32,39.
陈奋,闫冬梅 和赵忠明,2005.一种快速图像块填充算法及其在遥感影像处理中的应用.计算机应用.25(10)：2359-2361.
陈晋,何春阳和等,2001.基于变化向量分析(Cva)的土地利用/覆盖变化动态监测(Ⅱ)—变化类型的确定方法..遥感学报.5(5)：346-352,T003.
陈晋,何春阳和等,2001.基于变化向量分析的土地利用/覆盖变化动态监测(Ⅰ)——变化阈值的确定方法.遥感学报.5(4)：259-266,T003.
陈阳,陈鹰和林怡,2008.Gis辅助下的高分辨率遥感影像变化检测.铁道勘察.34(4)：29-32.
马建伟,2006.全色波段高空间分辨遥感影像特定目标变化检测技术研究,中国科学研究生院(遥感应用研究所).
黄慧萍 和吴炳方,2004.基于区域合并影像分割技术的多尺度地表景观分析.地理科学进展.23(3)：9-15,i001.
Argialas, D. and A. Tzotsos,2006. Automatic extraction of physiographic features and alluvial fans in Nevada, USA, from digital elevation models and satellite imagery through multiresolution segmentation and object-oriented classification:1?
Baraldi, A. and F. Parmiggiani,1996. Single linkage region growing algorithms based on the vector degree of match. Ieee Transactions on Geoscience and Remote Sensing.34(1):137-148.
Blaschke, T.,2004. Object based image analysis for remote sensing, Elsevier.65: 2-16.
Blaschke, T.,2010. Object based image analysis for remote sensing, Elsevier.65: 2-16.
Brown, K. M., G. M. Foody and P. M. Atkinson,2007. Modelling geometric and misregistration error in airborne sensor data to enhance change detection. International Journal of Remote Sensing.28(12):2857-2879.
Bruzzone, L. and D. F. Prieto,2000. Automatic analysis of the difference image for unsupervised change detection, IEEE.38:1171-1182.
Bruzzone, L. and S. B. Serpico,1997. An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images. Ieee Transactions on Geoscience and Remote Sensing.35(4):858-867.
Byrne, T. G., A. H. Mccain and T. M. Kretchun,1980. Testing Chrysanthemums for Disease Resistance. California Agriculture.34(11-1):14-15.
Canty, M. J.,2006. Image analysis, classification and change detection in remote sensing:with algorithms for ENVI/IDL. New York, CRC Press.
Canty, M. J. and A. A. Nielsen,2006. Visualization and unsupervised classification of changes in multispectral satellite imagery. International Journal of Remote Sensing.27(18):3961-3975.
Carleera, A., O. Debeirb and E. Wolffa,2004. Comparison of very high spatial resolution satellite image segmentations, Citeseer.5238:533.
Carvalho, L. M. T., L. M. G. Fonseca, F. Murtagh and J. Clevers,2001. Digital change detection with the aid of multiresolution wavelet analysis. International Journal of Remote Sensing.22(18):3871-3876.
Chatelain, F., J. Y. Tourneret, J. Inglada and A. Ferrari,2007. Bivariate gamma distributions for image registration and change detection. IEEE Transaction on image processing.16(7):1796-1806.
Chen, H. M., P. K. Varshney and M. K. Arora,2003. Performance of mutual information similarity measure for registration of multitemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing.41(11):2445-2454.
Cihlar, J., R. Latifovic, J. Chen, J. Beaubien, Z. Li and S. Magnussen,2000. Selecting representative high resolution sample images for land cover studies. Part 2: Application to estimating land cover composition. Remote Sensing of Environment. 72(2):127-138.
Congalton, R. G.,1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment.37(1):35-46.
Congalton, R. G. and K. Green,1998. Assessing the accuracy of remotely sensed data:principles and practices. New York, CRC Press.
Congalton, R. G., R. G. Oderwald and R. A. Mead,1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogrammetric Engineering and Remote Sensing.49:1671-1678.
Coppin, P., I. Jonckheere, K. Nackaerts, B. Muys and E. Lambin,2004. Digital change detection methods in ecosystem monitoring:a review. International Journal of Remote Sensing.25(9):1565-1596.
Coppin, P. R. and M. E. Bauer,1996. Digital change detection in forest ecosystems with remotely sensed data. Remote Sensing Reviews.13:207-234.
Dai, X. and S. Khorram,1997. Quantification of the impact of misregistration on the accuracy ofremotely sensed change detection.4.
Dai, X. and S. Khorram,1998. The effects of image misregistration on the accuracy of remotely sensed change detection, IEEE.36:1566-1577.
Dai, X. and S. Khorram,1998. The effects of image misregistration on the accuracy of remotelysensed change detection. IEEE Transactions on Geoscience and Remote Sensing.36(5):1566-1577.
Dempster, A. P., N. M. Laird and D. B. Rubin,1977. Maximum likelihood from incomplete data via the EM algorithm, JSTOR.39:1-38.
Geneletti, D. and B. G. H. Gorte,2003. A method for object-oriented land cover classification combining Landsat TM data and aerial photographs, Taylor & Francis. 24:1273-1286.
Gong, P.,1992. An efficient contextual classifier for land-use classification. Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium,. Houston, United States:555-557.
Gong, P., E. F. Ledrew and J. R. Miller,1992. Registration-noise reduction in difference images for change detection.13:773-779.
Green, K., D. Kempka and L. Lackey,1994. Using Remote-Sensing to Detect and Monitor Land-Cover and Land-Use Change. Photogrammetric Engineering and Remote Sensing.60(3):331-337.
Guo, Q., M. Kelly, P. Gong and D. Liu,2007. An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience & Remote Sensing.44(1):24-47.
Hotelling, H.,1936. Relations between two sets of variates, Biometrika Trust.28: 321-377.
Im, J., J. R. Jensen and J. A. Tullis,2008. Object-based change detection using correlation image analysis and image segmentation, Taylor & Francis, Inc. Bristol, PA, USA.29:399-423.
Johnson, D. W. and D. E. Todd,1998. Effects of harvesting intensity on forest productivity and soil carbon storage in a mixed oak forest. Management of Carbon Sequestration in Soil.351-363 457.
Kern, K., W. Sulzer and G. K. Lieb,2009. Detection of land-cover and land-use change in the Hohe Tauern National Park (Austria) using an object-based classification approach:first results. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX,. Berlin, Germany 7478:55.
Labovitz, M. L. and J. W. Marvin,1986. Precision in geodetic correction of TM data as a function of the number, spatial distribution, and success in matching of control points-A simulation. Remote sensing of environment.20:237-252.
Landgrebe, D.,1999. Information extraction principles and methods for multispectral and hyperspectral image data, Chapter.82:3?8.
Lu, D., P. Mausel, E. Brondizio and E. Moran,2004. Change detection techniques, Taylor & Francis.25:2365-2401.
Manandhar, R.,I. O. A. Odeh and T. Ancev,2009. Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement. Remote sensing of environment.1 (3).
Marceat, D. J., P. J. Howarth and J. M. Dubois,1990. Evaluation of the grey-level Co-occurrence matrix method for land cover classification using SPOT imagery. IEEE Transaction on Geoscience and remote sensing.28(4):513-519.
Marceau, D. J. and G. J. Hay,1999. Remote sensing contributions to the scale issue, Ottawa:Canadian Aeronautics and Space Institute,[1975]-c2004.25:357-366.
Markham, B. and T. J. R,1981. Land cover classification accuracy as a function of sensor spatial resolution Proceedings 15th on remote sensing of environment.
Meola, J. and M. T. Eismann,2008. Image misregistration effects on hyperspectral change detection.6966:69660Y.
Molinier, M., J. Laaksonen and T. Hame,2007. Detecting man-made structure and changes in satellite imagery with a content-based information retrieval system built on self-organizing map. IEEE Transaction on Geoscience and remote sensing. 45(4):861-874.
Moon, T. K.,1996. The expectation-maximization algorithm, IEEE.13:47-60.
Morisette, J. T. and S. Khorram,2000. Accuracy assessment curves for satellite-based change detection. Photogrammetric Engineering and Remote Sensing. 66(7):875-880.
Mouat, D. A., G. G. Mahin and J. Lancaster,1993. Remote sensing techniques in the analysis of change detection. Geocarto International.8(2):39-50.
Nielsen, A. A., K. Conradsen and J. J. Simpson,1998. Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: new approaches to change detection studies. Remote sensing of environment.64(1): 1-19.
Niemeyer, I. and M. J. Canty,2001. Knowledge-based interpretation of satellite data by object-based and multi-scale image analysis in the context of nuclear verification. IGARSS 2001 IEEE International Geoscience and Remote Sensing Symposium,9-13 July 2001, Sydeny, Australia,.
Niemeyer, I. and M. J. Canty,2003. Pixel-based and object-oriented change detection analysis using high-resolution imagery. Proc.25th Symposium on Safeguards and Nuclear Material Managment,13-15 May 2003 (CD-ROM). Stockholm.
Qian, Y., K. Zhang and F. Qiu,2005. Spatial contextual noise removal for post classification smoothing of remotely sensed images. Proceedings of the 2005 ACM symposium on Applied computing, Santa Fe, New Mexico ACM.
Quinlan, R.,2006. Rulequest research data mining tools. [Online].Available: http://www.rulequest.com/.
Read, J., J. M. Ferris and T. Jaffre,2002. Foliar mineral content of Nothofagus species on ultramafic soils in New Caledonia and non-ultramafic soils in Papua New Guinea. Australian Journal of Botany:50(5):607-617.
Roy, D. P.,2000. The impact of misregistration upon composited wide field of view satellite data and implications for change detection, Institute of Electrical and Electronics Engineers, Inc,445 Hoes Ln, Piscataway, NJ,08854-1331, UK.38: 2017-2032.
Salvador, E., A. Cavallaro and T. Ebrahimi,2004. Cast shadow segmentation using invariant color features. Computer vision and image understanding.95: 238-259.
Sasagawa, Y.,2005. Integrated shadow removal based on photogrammetry and image analysis. International Journal of Remote Sensing.26:18.
Schiewe, J., L. Tufte and M. Ehlers,2001. Potential and problems of multi-scale segmentation methods in remote sensing. GIS—Zeitschrift fur Geoinformationssysteme.6:34-39.
Singh, A.,1989. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing.10(6):989-1004.
Soille, P. and M. Pesaresi,2002. Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Transactions on Geoscience and Remote Sensing.40(9):2042-2055.
Stow, D. A.,1999. Reducing the effects of misregistration on pixel-level change detection, Taylor and Francis Ltd.20:2477-2483.
Stow, D. A. and D. M. Chen,2002. Sensitivity of multitemporal NOAA AVHRR data of an urbanizing region to land-use/land-cover changes and misregistration. Remote sensing of environment.80(2):297-307.
Strahler, A. H., C. E. Woodcock and J. A. Smith,1986. On the nature of models in remote sensing. Remote sensing of environment.20(2):121-139.
Townshend, J. R. G., C. O. Justice, C. Gurney and J. McManus,1992. The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing.30(5):1054-1060.
Van de Voorde, T., W. De Genst and F. Canters,2007. Improving pixel-based VHR land-cover classifications of urban areas with post-classification techniques. Photogrammetric Engineering & Remote Sensing.73(9):1017-1027.
Verbyla, D. L. and S. H. Boles,2000. Bias in land cover change estimates due to misregistration, London:Taylor & Francis, c1980-.21:3553-3560.
Walter, V.,2004. Object-based classification of remote sensing data for change detection. Photogrammetry and Remote Sensing.58(3-4):225-238.
Wang, L., W. P. Sousa and P. Gong,2004. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing.25(24):5655-5668.
Watanabe, S., K. Miyajima and N. Mukawa,1998. Detecting changes of buildings from aerial images using shadow and shading model.14th International Conference on Pattern Recognition (ICPR'98),August 16-August 20, Brisbane, Australia Published by the IEEE Computer Society.
Weismiller, R. A., S. J. Kristof, D. K. Scholz, P. E. Anuta and S. A. Momin, 1977. Change Detection in Coastal Zone Environments. Photogrammetric Engineering and Remote Sensing.43(12):1533-1539.
Xu, D., J. Z. Liu, Z. K. Liu and X. O. Tang,2004. Indoor shadow detection for video segmentation.2004 Ieee International Conference on Multimedia and Exp (Icme), Vols 1-3.41-44 2242.
Yang, X. and C. P. Lo,2000. Relative radiometric normalization performance for change detection from multi-date satellite images, ASPRS AMERICAN SOCIETY FOR PHOTOGRAMMETRY AND.66:967-980.
Zhou, W. and A. Troy,2008. An object-oriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing.29(11):3119-3135.
Zhou, W., A. Troy and M. Grove,2008. A Comparison of Object-Based with Pixel-Based Land Cover Change Detection in the Baltimore Metropolitan Area using Multitemporal High Resolution Remote Sensing Data. IGARSS 2008 IEEE International Geoscience and Remote Sensing Symposium, July 6-11,2008| Boston, Massachusetts, U.S.A.
Zorich, V.,1949. Communication in the presence of noise, Springer:55-75.
王文杰,赵忠明和朱海青,2009.面向对象特征融合的高分辨率遥感图像变化检测方法.计算机应用研究.(8)：3149-3151.
王伟平,2007.基于聚类分析的遥感图像变化检测方法研究,国防科学技术大学.
王广运,1996.差分GPS定位技术与应用.北京,电子工业出版社.
史培军,宫鹏和李晓,2000.土地利用覆盖变化研究的方法与实践.北京,科学出版社.
田新光,张继贤和张永红,2007.面向对象的红树林信息提取.海洋测绘.27(2)：41-44.
余鹏,封举富和童行伟,2005.一种新的基于高斯混合模型的纹理图像分割方法.武汉大学学报：信息科学版.30(6)：514-517.
李生平,1997.建筑工程测量.武汉,武汉工业大学出版社.
李向军,2006.遥感土地利用变化检测方法探讨,中国科学院研究生院(遥感应用研究院).
汪求来,2008.面向对象遥感影像分类方法及其应用研究,南京林业大学.
明冬萍,王群和杨建宇,2008.遥感影像空间尺度特性与最佳空间分辨率选择.遥感学报.(4)：529-537.
姜涛,马国锐 和秦前清,2005.基于遥感影像的变化检测技术.计算机应用研究.22(2)：255-257.
胡进刚,张晓东,沈欣 和张婵,2006.一种面向对象的高分辨率影像道路提取方法.遥感技术与应用.21(3)：184-188.
徐丽华,2005.顾及投影差的遥感影像变化检测,武汉大学.
崔林丽,史军,唐娉 和高志强,2005.中国陆地净初级生产力的季节变化研究.地理科学进展.24(3)：8-16,F003.
曹宝,秦其明,马海建和邱云峰,2006.面向对象方法在Spot5遥感图像分类中的应用——以北京市海淀区为例.地理与地理信息科学.22(2)：46-49,54.
梁俊,周武,陈川和董玉森,2008.基于滑动窗口法的遥感图像纹理统计信息提取研究.安徽农业科学.36(15)：6426-6428.
梅安新和彭望琭,2002.遥感概论.北京,高等教育出版社.
盛辉,廖明生和张路,2004.基于典型相关分析的变化检测中变化阈值的确定.遥感学报.8(5)：452-457.
廖明生和朱攀,2000.基于典型相关分析的多元变化的检测.遥感学报.4(3)：197-201.
廖明生和林晖,2003.雷达干涉测量——原理与信号处理基础.北京,测绘出版社.
蔡运龙,2001.土地利用/土地覆被变化研究：寻求新的综合途径.地理究.(6)：645-652.
霍春雷,程健,卢汉清和周志鑫,2008.基于多尺度融合的对象级变化检测新方法.自动化学报.34(3)：251-257.
鞠何其,2009.基于色彩空间变换的遥感影像阴影检测与去除技术上海交通大学.
刘小洲,2007.高分辨率遥感图像的变化检测技术研究,国防科学技术大学.
刘建军,2008.苍山县矿产资源开发遥感动态监测与景观生态重建潜力研究,山东师范大学.
刘慧平和朱启疆,1999.应用高分辨率遥感数据进行土地利用与覆盖变化监测的方法及其研究进展.资源科学.21(3)：23-27.
华媛媛,2009.纹理信息在遥感图像分类中的应用于研究,西安科技大学.
吴剑,2010.基于面向对象技术的遥感震害信息提取与评价方法研究,武汉大学.
张振龙,曾志远,李硕和胡子付,2005.遥感变化检测方法研究综述.遥感信息.(5)：64-66,59.
张路,2004.基于多元统计分析的遥感影像变化检测方法研究,武汉大学.
张汉松,2010.基于对象的海岸带地物变化检测技术的研究,浙江大学.
许妙忠和余志惠,2003.高分辨率卫星影像中阴影的自动提取与处理.测绘信息与工程.28(1)：20-22.
赵英时,2003.遥感应用分析原理与方法.北京,科学出版社.
邓劲松,石媛媛,陈利苏,王珂和祝锦霞,2009.基于近红外传感器和面向对象光谱分割技术的田间棉株识别与提取.光谱学与光谱分析(7)：1754-1758.
钟家强,2005.基于多时相遥感图像的变化检测,国防科学技术大学.
钟家强和王润生,2006.基于独立成分分析的多时相遥感图像变化检测.电子与信息学报.28(6)：994-998.
陈江龙,曲福田 和王启仿,2003.经济发达地区土地利用结构变化预测——以江苏省江阴市为例.长江流域资源与环境.12(4)：317-321.
陈建和窦贻俭,1996.地理信息系统支持下的城市生态环境功能区划.城市环境与城市生态.9(1)：24-30.
陈述彭,1990.遥感地学分析.北京,测绘出版社.
陈国宏,蔡彬清和李美娟,2007.元胞自动机：一种探索管理系统复杂性的有效工具.中国工程科学.9(1)：28-32,39.
陈奋,闫冬梅 和赵忠明,2005.一种快速图像块填充算法及其在遥感影像处理中的应用.计算机应用.25(10)：2359-2361.
陈晋,何春阳和等,2001.基于变化向量分析(Cva)的土地利用/覆盖变化动态监测(Ⅱ)—变化类型的确定方法..遥感学报.5(5)：346-352,T003.
陈晋,何春阳和等,2001.基于变化向量分析的土地利用/覆盖变化动态监测(Ⅰ)——变化阈值的确定方法.遥感学报.5(4)：259-266,T003.
陈阳,陈鹰和林怡,2008.Gis辅助下的高分辨率遥感影像变化检测.铁道勘察.34(4)：29-32.
马建伟,2006.全色波段高空间分辨遥感影像特定目标变化检测技术研究,中国科学研究生院(遥感应用研究所).
黄慧萍 和吴炳方,2004.基于区域合并影像分割技术的多尺度地表景观分析.地理科学进展.23(3)：9-15,i001.