摘要
岩石和矿物都是地质作用的产物，矿物是岩石的基本组成单位。矿物是由地质作用形成的天然单质或化合物，不同的矿物及矿物组合组成性质不同的各类岩石。矿物不仅包含不同的化学成分，而且具有一定的内部结构，随着外界条件的改变，其成分、结构会发生变化，形成新的矿物。岩石是由一种或多种矿物组成的，具有一定结构构造的集合体，广泛分布于地壳中，是地质作用发展的历史记录。
通过对岩石、矿物的研究可以探索和了解地壳的发展、变化及运动规律，为找矿及地质研究提供重要依据。矿产赋存于岩石中，伴随着地质作用而呈现出一定的规律，表现为一定的矿产类型总与一定的岩石类型有着依赖关系。展开对岩矿信息的地质研究工作能够为探测、查明、开产矿产资源提供地质资料，在矿产预测和开发中显示出岩矿研究工作的经济意义及实用价值。
遥感地质是综合应用现代遥感技术来研究地质规律，进行地质调查和资源勘察的一种方法。它从宏观的角度，着眼于由空中取得的地质信息，以各种地质体对电磁辐射的反应作为基本依据，结合其他各种地质资料的综合应用，以分析、判断、识别一定地区内的地质情况。近年来，随着多、高光谱遥感技术的发展，光谱分辨率的提高，遥感技术在岩矿信息识别和地质矿物识别填图等领域中的应用有了广泛的前景。
岩矿光谱特征与其物化属性的关联分析是遥感岩矿信息识别与提取的基础。这方面的研究大体包括从两方面进行：一是从岩石矿物特征光谱为切入点，结合岩石矿物的化学成分、晶体结构等物化知识进行相关性分析以提取岩矿信息；二是以物理模型为主，结合矿物光谱知识进行岩矿信息提取。目前，遥感技术用于岩矿信息识别及地质填图主要依赖于地物的光谱特征，是直接利用岩石矿物的光谱特征进行岩矿识别，定量、半定量提取岩矿信息。过度地依赖岩石、矿物的光谱信息往往忽略了矿物中化学成分与矿物、矿物与岩石之间的关系。大气辐射对地物波谱特征的影响、混合光谱造成的光谱漂移、变异等对单个波形的影响，导致多环节引入波谱特征对岩矿信息提取结果有较大的干扰。
本文基于遥感技术，结合地质研究方法提取青海省格尔木辖区内乌图美仁地区岩矿信息。分为两步进行：一是在该区区域地质背景下，基于遥感图像灰度及纹理特征，采用图像增强的一些方法，扩大图像中不同岩性之间的灰度及纹理差别，突出地物目标信息，使之易于目视解译，在此基础上建立感兴趣区，利用监督分类的方法对图像进行分类，达到区分不同岩性的目的；二是在此基础上，对不同的分类地区进行层层剥离。从对遥感波谱特征的分析出发，建立矿物中氧化物与光谱特征的光谱数学物理模型，借助标准矿物计算（CIPW）的岩石化学计算方法，按照岩矿光谱信息—氧化物—矿物—岩石的研究方法，对该区岩石、矿物进行识别并定量、半定量提取岩矿信息。本次研究从不同于以往的角度出发为遥感技术用于岩矿信息识别、提取矿物含量和化学成分等提供了一种新的思路。
主要研究成果和创新点如下：
1、研究基于不同源遥感数据的图像融合增强方法。
以ASTER数据和QucikBird-2数据为遥感数据源，实现了对多光谱图像和高分辨率图像的融合，得到了多光谱高分辨率影像，增强了多光谱图像的空间分辨率。实验表明，HIS加小波融合的方法在提高分辨率的同时，在最大熵值及光谱特征保持的定量评价下都取得较好的效果。增强后的图像在应用于感兴趣区建立以及图像分类时，可以解决由于高分辨率图像光谱分辨率不足而导致的解译、分类效果不佳、纹理粗糙等问题，空间及光谱分辨率增强后的融合图像提高了遥感图像整体质量和综合分析精度，分类效果明显优于融合前图像。
2、基于LS-SVM分类技术的高分辨率多光谱图像分类方法研究。
以最小二乘支持向量机（LS-SVM）分类技术为技术手段，基于融合后的高分辨率多光谱图像，提取图像中与岩性相关的纹理、形状、光谱信息，选取过程中以图像的光谱和纹理为主要特征信息，同时以J-M距离、转换分类度为依据选取最优特征空间，采用因子分析变换降维对特征空间进行压缩，使特征信息实现最优化，然后对已知样本进行训练，建立分类模型，评价模型精度。最后利用该模型对研究区进行岩性划分，并进行分类后处理。结果表明：加入纹理等信息后的LS-SVM分类模型更加利于岩性的判别；基于LS-SVM的分类方法在遥感图像岩性识别中表现良好。
3、利用支持向量机回归方法改进了氧化物质量分数定量反演的精度。
矿物中氧化物的反演不是一个简单的线性问题。在对比了一些线性回归算法与支持向量机回归算法之间的回归效果之后，提出了基于支持向量机的回归模型，并针对这个模型从核函数方面进行了改进，改进后的回归模型提高了氧化物的反演精度。实验表明，在提供同样数量样本的情况下，该方法反演结果更加准确，应用效果更好。
4、将CIPW岩石化学计算方法引入到遥感地质矿物定量识别中，提出“岩矿光谱信息—氧化物—矿物—岩石”的岩矿信息提取方法。
将CIPW岩石化学分析方法引入到遥感地质工作中来，把遥感图像中的每一个像元作为一个岩石样本，以典型岩石、矿物的诊断性波谱特征为基础，建立矿物中氧化物与光谱特征的光谱数学物理模型，对每个像元计算其化学成分，然后进行CIPW标准矿物计算，得到标准矿物定量信息，最后按照岩矿光谱信息—氧化物—矿物—岩石的研究思路进行计算，得到了该区定量、半定量岩矿信息提取结果。
5、根据地质成岩理论建立岩性识别规则，提取岩性信息。
岩石中矿物或矿物组合按一定的规律共同存在，反映着统一的形成条件，依一定的地质作用而共同组合。岩石由于成因的不同和外在条件的改变，所包含的矿物也会发生变化。从地球化学的观点来看，同一地层内的不同岩性所含的化学元素是多样的，各元素的含量又是非常不均匀的，从而导致其氧化物、矿物含量也有所不同。再从岩石学的角度来看，不同的岩性必然是由种类繁多及多种组合矿物形成。基于地质理论，在已经圈定的岩性分类基础上，以不同岩石中氧化物含量、矿物含量差异为基准，建立岩性识别规则，按照层层剥离的原则，对研究区内的岩性进行分类识别。
Rocks and minerals are the products of geological processes. Mineral is the basicunit of rock. The mineral is a simple substance or compounds formed by geologicalprocesses. Different minerals or the combinations of them compose various types ofrocks of different nature. The minerals contain different chemical compositions andhave certain internal structures. As the external conditions change, the compositionsand structures also make some changes that may lead to the formations of newminerals. Rock is the assemblage of one or more kinds of minerals aggregating with acertain texture and structure. It is widely distributed in the earth's crust and is therecord of the history of geological developments.
Through the study of rocks and minerals, both the exploration and theunderstanding of crust development, changes as well as the laws of movements can beachieved so that bring about important inferences for prospecting and geologicalstudies. Minerals host in the rocks, along with geological processes and show certainregularity of the dependency of the type of rocks on the types of the minerals.Launching the geological study of the rock and mineral information can providegeological data and material for exploring, probing, and mining the mineral resourceswhich shows the economic significance and practical value of the rock and mineralresearch work in the mineral prediction and development.
Remote sensing in geology is a technique that comprehensively applies modernremote sensing techniques to study the geological rules as well as carry out geologicalsurvey and resources exploration. It focuses on the geological information obtainedfrom the air from a macro perspective and takes the reactions of a variety of geologicbodies of electromagnetic radiation as a fundamental basis. This technique iscombined with a variety of other integrated applications of geological data so that itcan be used to analyze, to judge, and to identify certain areas of geological situation.In recent years, with the development of multi-spectral and hyper-spectral remotesensing technology as well as the improvement of spectral resolution, the applicationsof remote sensing technology have good prospects of development in several fieldssuch like the identification of rocks and minerals, the identification of minerals andmapping and so on.
Associated analysis of spectral features of rocks and minerals together with theirmaterialization properties is the basis of the identification and extraction of rocks andminerals via the means of remote sensing. The research in this area can be generally divided into two aspects: one is to take the characteristic spectra of rocks and mineralsas a starting point and carry out relevance analysis by taking the chemicalcomposition of rocks and minerals, the crystal structure or some other physical andchemical knowledge into consideration to extract the rock and mineral information;another is to put emphasis on the physical model and extract the information of rocksand minerals with the knowledge of spectra of them. Currently, remote sensingtechnology in the identification of information of rocks and minerals as well as thegeological mapping are mainly dependent on the spectral characteristics of the surfacefeatures. It directly makes use of the spectral characteristics of the rocks and mineralsto identify them and extracts their information quantitatively or semi-quantitatively.Excessive reliance on the spectral information of rocks and minerals often results inthe overlook of the relationship between the chemical composition of minerals andmineral or the mineral and the rocks. The influence on the characteristics of objectspectrum that is caused by the atmospheric radiation and the influence on the singlewaveform caused by the spectral shift or the mutation that is brought about by themixed spectrum may result in the repeated introductions of the spectral characteristicsand may lead to greater interference of the results of rock and mineral informationextraction.
Based on remote sensing technology, this paper combines itself with geologicalresearch methods to extract the information of rocks and minerals within Wutumeirenin the area of Golmud in Qinghai Province. The study can be divided into two steps:First, in geological background of this region that mentioned above, the author adoptsthe methods of image enhancement based on the gray level and texture features of theremote sensing images and expand the differences in the gray level and texturefeatures between various lithological characters in order to give prominence to theinformation of the surface features. This makes it easy for the visual interpretation andestablishment of the regions of interest. Besides, this paper uses the method ofsupervised classification to classify the images to meet the purpose of distinguishingdifferent lithological characteristics. Moreover, it also separates the regions ofdifferent classifications.
From the analysis of the spectral characteristics viewed by the techniques ofremote sensing, the author establishes the mathematical and physical models of therelationships between the oxides in the minerals and the characteristics of thespectrum. Via using the chemical computing method of Norm Mineral Calculation(CIPW) for the rocks, according to the method of spectral information–oxide–mineral–rock, this paper carries out the identification of the rocks and mineralsin the certain region as well as the extraction of the information of the rocks andminerals quantitatively or semi-quantitatively.
This research carries out investigation from the angle that is quite different fromthe ones taken in the previous studies and provides a new way of thinking about the identification of the information of rocks, the content of the mineral content and thechemical composition, etc.
The main research results and innovations of this research are as following:
1.The investigation of the method of the integration of image enhancementbased on different remote sensing data of multiple sources.
Taking the ASTER data and QucikBird-2data as the data sources, this paperrealize the target of the integration of multi-spectral images and high resolutionimages and gets the images with the good qualities of both the two kinds of images sothat it improves the spatial resolution of multispectral images. The experiments showthat the HIS wavelet fusion method can improve the resolution; at the meanwhile, itcan also achieved good results in the quantitative evaluations of the maximum entropyas well as the maintenance of spectral features.
When applied to the establishment of the regions of interest and the classificationof images, the enhanced images can solve the problems of the low quality ofinterpretation and classification, the coarseness of the texture that are caused by theinsufficient spectral resolution of the high-resolution images. This in turn brings aboutthe improvement of the quality of the integrated image and the precision of thecomprehensive analysis. The classification based on these images is better than othersbased on the images without the integration.
2.The classification methods based on the LS-SVM classification techniqueswith high-resolution multi-spectral image.
Using least squares support vector machine (LS-SVM) classification techniques,based on merged high-resolution multi-spectral image, texture, shape and spectralinformation of image with the relevance of lithology were extracted. Spectral andtexture of image were treated as the main feature information in the process ofselecting. Based on JM distance and conversion of classification degree, the optimalfeature space was selected. Feature space was compressed in factor analysis totransform the dimensionality reduction so that feature information achieved optimaleffect. Training the known samples, establishing classification model and evaluatingthe accuracy of the model followed. Finally, the study area was dealt with lithologypartition and classification post-processing by the model. The results showed that:LS-SVM classification model with the texture and other information were moreconducive to lithology discrimination; the LS-SVM classification method performedwell in the remote sensing images lithology identification.
3.Support vector machine regression method improves the accuracy ofquantitative retrieval of oxide weight percent.
The retrieval of the mineral oxide is not a simple linear problem. Aftercontrasting the regression effect between some linear regression algorithms andsupport vector machine regression algorithm, support vector machine regressionmodel was proposed and improved in terms of kernel function. The improved regression model improved retrieval accuracy of the oxide. The experiments show thatthe retrieval result of the method is more accurate and its application effect is better inthe case of the same number of samples.
4.The CIPW rock chemistry calculation method is introduced into quantitativeidentification of remote sensing geological mineral and put forward a″rock andmineral spectral information-oxide-mineral-rock″method of extracting rock andmineral information.
The CIPW rock chemical analysis method is introduced into the remote sensinggeological work, regarding each pixel of remote sensing images as a rock sample.Based on typical rock and diagnostic spectral characteristics of the mineral, aspectral mathematical physics model of oxide in the mineral and the spectralcharacteristics of mineral were established. The chemical composition of each pixelwas calculated. Then proceed to the CIPW Standard mineral calculation to get thestandard mineral quantitative information.
At last, calculating according to the rock and mineral spectral information-oxide-mineral-rock research idea, we will get quantitative and semi-quantitative rockand mineral information in this area.
5.According to the theory of geological diagenesis, lithology identification ruleswere established to extract lithology information.
Minerals and mineral assemblages according to certain rules exist in the rock. Itreflects the uniform formation conditions. They combine according to a certaingeological effect. Due to different causes of formation and the changes of externalconditions, the mineral which rocks contain will change. From the geochemical pointof view, the chemical elements contained in different rocks in the same stratum isdiverse and the content of each element is very uneven, resulting in the contents ofoxide and mineral are also different. From the petrological point of view, differentlithology must be formed by a wide range and variety of combinations of minerals.Based on geological theory, the delineated lithology classification is seen as abenchmark. Based on the content of oxide in different rocks and the difference ofmineral content, the lithology identification rules is established. According to theprinciple of layer stripping identification, the lithology in the study area can beclassified.
引文
[1]金庆花,朱丽丽,张立新,等.矿产资源评价与矿山环境监测中高光谱遥感技术方法应用的实例[J].地质通报,2009,28(2～3):278-284.
[2]章革.高光谱短波红外技术在矿区矿物填图中的应用研究[D].北京:中国地质大学,2004.
[3]甘甫平,王润生,马葛乃,等.光谱遥感岩矿识别基础及技术研究进展[J].遥感技术与应用,2002,17(3):140-147.
[4] Hunt G R, Salisbury J W, and Lenhoff C J. Visible and near infrared spectra ofminerals and rocks: III Oxides and hydroxides[J]. Modern Geology,1978,(2):195-205.
[5] Lawrence C R, Robert G S, John C M. Distribution of Hydrothermally AlteredRocks in the Reko Diq, Pakistan Mineralized Area Based on Spectral Analysis ofASTER data[J]. Remote Sensing of Environment,2006,104:74-87.
[6] Clark R N. Spectroscopy of Rocks and Minerals, and Principles of Spectros-copy[C]. King P L, Ramsey M S, Swayze G A. Infrared Spectroscopy inGeochemistry, Exploration Geochemistry, and Remote Sensing. London:Mineralogical Association of Canada,2005.
[7] Rutz Armenta J R, Prol-Ledesma R M. Techniques for enhancing the spectralresponse of hydrothermal alteration minerals in Thematic Mapper images ofCentral Mexico[J]. International Journal of Remote Sensing,1998,(19):1981-2000.
[8] Lenio S G, Raimundo A F, Icaro V. Spectral discrimination of hydrothermallyaltered materials using ASTER short-wave infrared bands: evaluation in atropicalsavannah environment[J]. Internation Journal of Applied Earth Observation andGeoinformation,2005,(7):107-114.
[9] Tommaso I D, Rubinstein N. Hydrothermal alteration mapping using ASTERdata in the Infiernillo porphyry deposit, Argentina[J]. Ore Geology Reviews,2006,32(1-2):275-290.
[10]Gaffey S J. Spectral reflectance of carbonates minerals in the visible and nearinfrared(0.35～2.55μm): anhydrous carbonate minerals[J]. Geophysical Resear-ch,1987,92(B2):1429-1440.
[11]Clark R N, King T V V, Klejwa M, et al. High Spectral Resolution ReflectanceSpectroscopy of Mineral[J]. J.Geophys Res,1990,95:12653-12680.
[12]Post J L, Noble P N. The near-infrared combination and frequencies ofdioctahedral smectites, micas and illites[J]. Clays and clay minerals,1993,41(6):639-644.
[13]Duke E F. Near infrared spectra of muscovite, Tschermak substitution, andmetamorphic reaction progress: implications for remotesensing[J]. Geology,1994,22:621-624.
[14]王润生.成像光谱技术开发应用研究[R].北京:航空物探遥感中心,1999.
[15]甘甫平,王润生.遥感岩矿信息提取基础与技术方法研究[M].北京:地质出版社,2003.
[16]徐进元.面向找矿的高光谱遥感岩矿信息提取方法研究[D].武汉:中国地质大学,2009.
[17]李武跃.遥感影像纹理特征提取及其定量分析[D].上海:上海交通大学,2008.
[18]王昆,万幼川,屈颖.融合纹理和形状特征的高分辨率遥感影像道路提取[J].理论研究,2010:7-11.
[19]Chen Jie, Deng Min, Xiao Peng-feng, et al. Optimal spatial scale choosing forhigh resolution imagery based on texture frequency analysis[J]. Journal ofRemote Sensing,2011:1007-4619.
[20]孟宇.基于PSO的纹理合成及其应用[D].长春:吉林大学,2002.
[21]胡玉福,邓良基,匡先辉,等.基于纹理特征的高分辨率遥感图像土地利用分类研究[J].地理与地理信息科学,2011,27(5):42-45.
[22]陈超详,陈华锋,叶时平.高分辨率影像中基于纹理的建筑区信息提取[J].计算机工程,2011,37(21):126-130.
[23]李培军.用ASETR图像和地统计学纹理进行岩性分类[J].矿物岩石,2004,24(3):116-120.
[24]Chica-Olmo M, Abarca-Hernández F. Computing geostatistical image texture forremotely sensed data classification[J]. Computers&Geosciences,2000,26(4):373-383.
[25]张自力,秦其明,曹宝,等.高分辨率遥感影像在岩墙地质体信息提取中的应用[J].地理与地理信息科学,2007,23(3):15-18.
[26]何凯涛,甘甫平,王永江.高空间分辨率卫星遥感地质微构造及蚀变信息识别[J].国土资源遥感,2009,1(79):97-99.
[27]Haralick R M, Shanmugam K, Dinstein I. Texture feature for image classific-ation[J]. IEEE Transactions on Systems, Man, and Cybermetics,1973,3:610-621.
[28]郝敏钗,杨晓波,冯冀宁.基于尺度和灰度共生矩的纹理图像分割[J].石家庄职业技术学院学报,2007,19(2):3-5.
[29]薛笑荣,张艳宁,赵荣椿.利用小波尺度共生矩阵和灰度共生矩阵的SAR图像分类[J].中国图形学与图像分析,2002,7(4):193-197.
[30]赵莹.基于分形几何学的图像信息表示及其应用研究[D].合肥:合肥工业大学,2009.
[31]郭建明.分形理论在遥感影像空间尺度转换中的应用研究[D].西安:西北大学,2008.
[32]史革萌.基于分形几何的数学图像处理[J].科技信息,2010,(29):178-179.
[33]徐志洁.基于边缘检测及Markov随机场模型的遥感图像分割研究[D].北京:中国农业大学,2005.
[34]舒坚,胡茂林.基于Markov随机场模型的纹理图像的缺陷检测[J].计算机技术与发展,2006,16(5):65-67.
[35]李小涛.地统计学和神经网络在遥感影像分类中的应用研究[D].济南:山东科技大学,2004.
[36]刘灵丽.指纹图像预处理与特征提取[D].长沙:湖南大学,2005.
[37]Hubbard B E, Crowley J K, Zimbelman D R. Comparative alteration mineralmapping using visible to shortwave infrared (04-2.4μm) Hyperion, ALI, andASTER Imagery[J]. IEEE Transactions of Geosciences and Remote Sensing,2003,41(6):1401-1410.
[38]Kruse F A, Boardman J W, Huntington J F. Comparison of airborne hyperspectraldata and EO-1Hyperion for mineral mapping[J]. IEEE Transacti-ons ofGeosciences and Remote Sensing,2003,41(6):1388-1400.
[39]Freek van der Meera.b. Analysis of spectral absorption features in hyperspectralimagery[J]. international Journal of Applied Earth Observation andGeoinformation,2003,5(1):55-68.
[40]Launeau P, Girardeau J, Sotin C, et al. Comparison between field measurementsand airborne visible and infrared mapping spectrometry (AVRIS and HyMap) ofthe Ronda peridotite massif (south-west Spain)[J]. International Journal ofRemote Sensing,2004,25(14):2773-2792.
[41]Bi Xiao-jia, Miao Fang, Li Jia-guang. Hyperion Hyperspectral Remote SensingLithology Identification and Mapping[C].2010International Conference onRemote Sensing. Hangzhou: ICRS,2010:153-156.
[42]Drake N A, Mackin S, et al. Mapping vegetation,soils,and geology in semiaridshrublands using spectral matching and mixture modeling of SWIR AVIRISimagery[J]. Remote Sensing of Environment,1999,1(68):12-25.
[43]Calvin W M, Vaughan R G, Taranik J V, et al. Mapping natural and humaninfluenced acid sulfate weathering near Reno, NV using the SEBASShyperspectral instrument[J]. IEEE International Geoscience and Remote SensingSymposium,2001,2:727-729.
[44]Crósta A P, Sabine C, Taranik J V. Hydrothermal alteration mapping at Bodie,California, using AVIRIS hyperspectral data[J]. Remote Sensing of Environment,1998,65(3):309-319.
[45]Rowan L C, Crowley J K, Schmidt R G, et al. Mapping hydrothermally alteredrocks by analyzing hyperspectral image (AVIRIS) data of forested areas in theSoutheastern United States[J]. Journal of Geochemical Exploration,2000,68(3):145-166.
[46]万余庆,闫永忠.高光谱技术在汝箕沟煤田烧变岩和Fe～(3+)丰度信息提取中的方法研究[J].国土资源遥感,2003,2:50-54.
[47]刘天乐.基于高光谱遥感的矿物光谱特征分析和提取[D].北京:中国地质大学,2009.
[48]田丰.全波段(0.35～25μm)高光谱遥感矿物识别和定量化反演技术研究[D].北京:中国地质大学,2010.
[49]张宗贵,王润生.基于谱学的成像光谱遥感技术发展与应用[J].国土资源遥感,2000,3:16-37.
[50]王钦军.高/多光谱遥感目标识别算法及其在岩性目标提取中的应用[D].北京:中国科学院遥感应用研究所,2006.
[51]田丰,董丽娜,杨苏明,等.混合矿物组合光谱在蚀变矿物填图中的应用-以云南香格里拉地区Hyperion数据蚀变矿物填图为例[J].地质与勘探,2010,46(2):0331-0337.
[52]毕晓佳.高光谱遥感岩矿填图应用研究[D].成都:成都理工大学,2009.
[53]浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000.
[54]徐元进.面向对象的高光谱遥感岩矿信息提取方法研究[D].长春:吉林大学,2009.
[55]Pearlman J S, Mendenhall J A, Reuter D. Overview of the Hyperion imagingspectrometer for the NASA EO-1mission[J]. IEEE Geoscience and RemoteSensing,2002,7:3036-3038.
[56]Carman S, Segal C, Jarecke P, et al. Preprocessing EO-1Hyperion hyperspectraldata to support the application of agricultural indexes[J]. IEEE Transactions onGeosciences and Remote Sensing,2003,41(6):1246-1259.
[57]VAPNIK V N.张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000.
[58]黄亮,舒宁.基于支持向量机（SVM）单目标SAR图像分割[J].地理空间信息,2011,9(1):60-65.
[59]王众,张哨楠,匡建超,等.基于主成分分析和支持向量机的PCA-SVM储层识别模型研究[J].物探化探计算技术,2010,32(6):636-640.
[60]赵书河,冯学智,都金康,等.基于支持向量机的SPIN-2影像与SPOT-4多光谱影像融合研究[J].遥感学报,2003,7(5):407-411.
[61]王茂盛.高分辨率遥感影像军用目标检测研究[D].兰州:兰州大学,2007.
[62]员永生.基于支持向量机分类的面向对象土地覆被图像分类方法研究[J].西安:西北农林科技大学,2010.
[63]Huang C, Davis L S, Townshend J R G. An assessment of support vectormachines for land cover classification[J]. International Journal of RemoteSensing,2002,23:725-749.
[64]Martin B, Hugh G L, Steve R G. Linear spectral mixture models and supportvector machines for remote sensing[J]. IEEE Transactions on Geoscience andRemoteSensing,2000,38(5):2346-2360.
[65]谭琨,杜培军.基于支持向量机的高光谱遥感图像分类[J].红外与毫米波学报,2008,27(2):123-128.
[66]夏建涛.基于机器学习的高维多光谱数据分类[D].西安:西北工业大学,2002.
[67]Keuchel J, Naumann S, Heiler M, et al. Automatic land cover analysis forTenerife by supervised classification using remotely sensed data[J]. RemoteSensing of Environment,2003,86(4):530-541.
[68]Melgani F, Bruzzone L. Classification of hyperspectral remote sensing imageswith support vector machines[J]. IEEE Transactions on Geoscience and RemoteSensing,2004,42(8):1778-1790.
[69]Inglada J. Automatic recognition of man-made objects in high resolution opticalremote sensing images by SVM classification on geometric image features[J].Isprs Journal of Photogrammetry and Remote Sensing,2007,62(3):236-248.
[70]何灵敏,沈掌泉,孔繁胜,等. SVM在多源遥感图像分类中的应用研究[J].中国图象图形学报,2007,12(4):649-654.
[71]王圆圆,陈云浩,李京.基于支持向量机（SVM）特征加权/选择的光谱匹配算法[J].光谱学与光谱分析,2009,29(3):735-739.
[72]黄景涛,马龙年,钱积新.一种用于多分类问题的改进支持向量机[J].浙江大学学报（工学版）,2004,38(12):1633-1636.
[73]薛云.基于蚁群算法和支持向量机的矿化蚀变信息提取研究[D].长沙:中南大学,2007.
[74]张睿,马建文.改进的P-SVM支持向量机与遥感数据分类[J].遥感学报,2009,(3):445-452.
[75]叶瑛.中酸性火成岩岩石化学计算若干问题的讨论及解决方案[J].岩石学报,1987,3:74-82.
[76]刘宝良. CIPW标准矿物计算法应用时存在问题的探讨[J].地质与资源,2001,10(3):180-189.
[77]王文广.一种计算岩石中矿物组成的新方法[J].铀矿地质,2009,25(6):344-347.
[78]罗慧芬.东昆仑遥感矿化蚀变信息提取方法研究[D].成都:成都理工大学,2010.
[79]韦振丽.遥感矿化蚀变信息提取在肯德可克—野马泉地区找矿预测中的应用[D].广州:中南大学,2009.
[80]冯聪.内蒙古白乃庙地区矿化蚀变信息提取及成矿预测[D].北京:中国地质大学,2006.
[81]黄舒城.土壤粘土矿物混合光谱分解方法研究[D].吉林:吉林大学,2010.
[82]李假广.东昆仑岩矿光谱数据测量方法研究及特征分析[D].成都:成都理工大学,2010.
[83]丑晓伟,傅碧宏.干旱区TM图像岩石地层信息提取与分析方法研究[J].沉积学报,1995,13:164-170.
[84]刘玉英,郝福江.遥感地质学[M].北京:地质出版社,2010.
[85]王雷光,刘国英,梅天灿.一种光谱与纹理特征加权的高分辨率遥感纹理分割算法[J].光学学报,2009,29(11):3010-3017.
[86]黄昕,张良培,李平湘.基于小波的高分辨率遥感影像纹理分类方法研究[J].武汉大学学报（信息科学版）,2006,31(1):66-69.
[87]冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究[J].北京测绘,2007,3:19-22.
[88]朱黎江,亲其明,陈思锦. ASTER遥感数据解读与应用[J].国土资源遥感,2003,2(56):59-63.
[89]曲春成,梁立恒,王明常,等. ASTER数据在吉林市土地利用调查中的应用[J].测绘与空间地理信息,2009,32（3）:7-11.
[90]郇正良.基于小波变换的纹理图像分割算法研究[D].青岛:山东科技大学,2003.
[91]邓洁,夏春林,王润芳.基于HIS变换和WT变换的遥感影像融合[J].测绘与空间地理信息,2009,32(1):107-110.
[92]王攀峰,杜云飞,周海芳,等.遥感图像像素级融合方法的建模与分析[J].计算机工程与科学,2007,29（11）:22-25.
[93]吴均,赵忠明.利用基于小波的尺度共生矩阵进行纹理分析[J].遥感学报,2001,5(2):100-103.
[94]徐胜详,胡超,徐运清.利用Matlab实现基于小波变换的遥感图像融合[J].微计算信息,2008,24(1-3):302-303.
[95]雷金波.基于逻辑回归和支持向量机的设备状态退化评估与趋势预测研究[D].上海:上海交通大学,机械设计及理论,2008.
[96]曹敏,史照良,沈泉飞. HIS变换和提升小波变换相结合的影像融合模型研究[J].测绘通报,2009,(3):20-23.
[97]孙华,林辉,熊育久,等. SPOT5影像统计分析及最佳组合波段选择[J].应用技术,2006:57-61.
[98]陆欢,吴庆宪,姜长生.基于PCA与小波变换的彩色图像融合算法[J].计算机仿真,2007,24(9):202-206.
[99]喻怀义,朱谷昌,杨自安,等. ASTER多光谱波段与SPOT全色波段融合方法研究[J].矿产地质,2008,22(1):69-73.
[100] Scholkopf B, Mika S, Burges C. Input space vs. feature space in kernel-basedmethods[J]. Transactions on Neural Networks,1999,10(5):1000-1017.
[101]傅文杰,洪金益,朱谷昌,等.基于SVM遥感矿化蚀变信息提取研究[J].国土资源遥感,2006,2（68）:16-19.
[102]邹敏.基于支持向量机的水电机组故障诊断研究[D].武汉:华中科技大学,2007.
[103] Suykens J A K, Vandewalle J. Least Squares Support Vector MachineClassfiers[J]. Neural Processing Letter,1999,9:293-300.
[104]张全明,刘会金.最小二乘支持向量机在电能质量扰动分类中的应用[J].中国电机工程学报,2008,28(1):106-110.
[105]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].浙江:浙江大学,2008.
[106] JPL. ASTER Spectral Library Version1.2[EB/OL].[2002] http://speclib.jlp.nasa.gov.
[107]米红,张文璋.实用现代统计分析方法与SPSS应用[M].北京:当代中国出版社,2000.
[108]邢宇,姜琦刚,李远华,等.基于RS和GIS的松辽平原黑土信息提取[J].农业工程学报,2010,26(10):212-216.
[109]谢清俊. SVM自适应核设计[D].武汉:武汉科技大学,2007.
[110] Lin C J. On the convergence of the decomposition method for support vectormachines[J]. IEEE Transactions on Neural Networks,12(6):1288-1298.
[111]林涛.基于支持向量机回归（SVR）的材料热加工过程建模[D].上海:上海交通大学,2010.
[112]王臻.基于支持向量机的生物质气化过程建模及优化研究[D].石家庄:华北电力大学,模式识别语与智能系统,2008.
[113]王振友,叶丽婷,牛庆敏.大气中臭氧含量分析预测的支向量机模型[J].数学的实践与认识,2008,38（9）:52-56.
[114]刘倩.基于分形理论及R/S方法的股票分析[D].无锡:江南大学,2009.
[115]宫幼林.基于图像分割和支持向量机的半自动图像标记系统的研究[D].济南:山东大学,2008.
[116]白裔峰.偏最小二乘算法及其在基于结构风险最小化的机器学习中的应用[D].成都:西南交通大学,2007.
[117] Chih C J. LS-SVM: A library for support vector machines[EB/OL].[2001]http://www.csie.ntu.edu.tw/~cjlin/LS-SVM.
[118] Eberhart R, Kennedy J. A new optimization using particle swarm theory[C].In proceedings of the6th international symposium on micro machine and humanscience. Piscataway, NJ: IEEE Service Center,1995:39-43.
[119]任江涛,赵少东,许盛灿,等.基于二进制PSO算法的特征选择及SVM参数同步优化[J].计算机科学,2007,34(6):179-182.
[120]袁小艳,刘爱伦.基于PSO算法的支持向量机核参数选择问题研究[J].控制理论与应用,2007,26(5):5-8.
[121]鞠秋文. PSO-SVM算法在网络入侵检测中的研究[J].计算机仿真,2011,28(4):130-148.
[122]杜荣军.基于数值实验的采煤沉陷与地质影响因素量化关系研究[D].西安:西南科技大学,2008.
[123]王洪英.火成岩岩石可钻性分析研究[D].大庆:大庆石油学院,2008.
[124]李亚美,严寿鹤,陈国勋,等.地质学基础[M].北京:地质出版社,1984.
[125]邱家骧.岩浆岩岩石学[M].北京:地质出版社,1985.
[126]贺同兴,卢兆良,李树勋,等.变质岩岩石学[M].北京:地质出版社,1980.