“全域—局部”不透水面信息遥感分步提取模型
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摘要
提出"全域—局部"遥感信息分布提取模型,通过计算和整合影像局部范围内的空间和光谱特征来优化全域上光谱混淆较大像元的提取精度。模型分为两个主要计算步骤:"全域"前分类与"局部"后分类;"全域"前分类将仅划分出满足一定精度阈值标准的像元,而"局部"后分类则在此部分分类结果基础上,进一步发掘和计算已分类像元所蕴含的信息来辅助对全域未分类像元的提取。在不透水面专题提取过程中,采用支持向量机SVM作为前分类器,通过控制精度阈值所对应的分类后验概率产生部分分类结果;采用调节最小距离分类器作为后分类器,根据一定的权重整合像元局部范围内的空间与光谱信息,代替了传统的全域光谱信息来优化分类。实验采用TM5影像以及所对应的NLCD(National Land Cover Data)标准不透水面产品作为测试集,"全域-局部"模型对应单一SVM模型的提取精度由80.31%提高为82.73%,局部后分类器精度较单一SVM模型由54.27%提高到59.94%。实验证明该模型具有较明显的精度提升且能够较好地解决不透水面与裸土混淆的问题,并得到空间形态上更为完善的不透水面提取结果。
This paper presents a"global-local"remote sensing information extract model,which extracts and integrates the spatial and spectral characteristics within the images' local area. The model can optimize the accuracy of extraction on the pixels with spectral fuzzy. The model can be briefly described into two steps: "global"prior classifier and"local"posteriori classifier. The"global"priori classifier will only classify pixels which are above certain accuracy thresholds,and the "local"posteriori classifier will further explore the information of the already classified pixels from the partial-classified results. The local information will be used to classify the unclassified pixels at the global scale. When extraction of Impervious Surface Area( ISA) experiment,we used Support Vector Machine( SVM) as a priori classifier,which is controlled by an accuracy threshold to output the partial-classified results. We also used an Adjust Minimize Distance Classifier( AMDC) as the posteriori classifier,which integrates the spatial information within local area around the unclassified pixels to classify the pixels with high degree of difficulty of classification by only spectral information. The experiment on the Landsat TM5 image and corresponding National Land Cover Database( NLCD) pro-d ucts as reference indicates that"global-local"model enhanced the accuracy from 80. 31%,which is provided by SVM model,to 82. 73%. Meanwhile,the accuracy of posteriori classifier was enhanced from 54. 27%( SVM) to 59. 94%. The results proved that combine with spatial and spectral information is an effective way for ISA extraction and the"globle-local"model can improve the accuracy of ISA extraction and can obtain more spatially explicit results.
This paper presents a"global-local"remote sensing information extract model,which extracts and integrates the spatial and spectral characteristics within the images' local area. The model can optimize the accuracy of extraction on the pixels with spectral fuzzy. The model can be briefly described into two steps: "global"prior classifier and"local"posteriori classifier. The"global"priori classifier will only classify pixels which are above certain accuracy thresholds,and the "local"posteriori classifier will further explore the information of the already classified pixels from the partial-classified results. The local information will be used to classify the unclassified pixels at the global scale. When extraction of Impervious Surface Area( ISA) experiment,we used Support Vector Machine( SVM) as a priori classifier,which is controlled by an accuracy threshold to output the partial-classified results. We also used an Adjust Minimize Distance Classifier( AMDC) as the posteriori classifier,which integrates the spatial information within local area around the unclassified pixels to classify the pixels with high degree of difficulty of classification by only spectral information. The experiment on the Landsat TM5 image and corresponding National Land Cover Database( NLCD) pro-d ucts as reference indicates that"global-local"model enhanced the accuracy from 80. 31%,which is provided by SVM model,to 82. 73%. Meanwhile,the accuracy of posteriori classifier was enhanced from 54. 27%( SVM) to 59. 94%. The results proved that combine with spatial and spectral information is an effective way for ISA extraction and the"globle-local"model can improve the accuracy of ISA extraction and can obtain more spatially explicit results.
引文
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程熙,沈占锋,骆剑承,朱长明,周亚男,胡晓东.2011.利用混合光谱分解与SVM估算不透水面覆盖率.遥感学报,15(6):1235-1247
de Jong S M,Hornstra T and Maas H G.2001.An integrated spatial andspectral approach to the classification of Mediterranean land cover types:the SSC method.International Journal of Applied Earth O bservation and Geoinformation,3(2):176-183[DOI:10.1016/S0303-2434(01)85009-1]
Franke J,Roberts D A,Halligan K and Menz G.2009.Hierarchical Multiple Endmember Spectral Mixture Analysis(MESMA)of hyperspectral imagery for urban environments.Remote Sensing of Environment,113(8):1712-1723[DOI:10.1016/j.rse.2009.03.018]
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骆剑承,盛永伟,沈占锋,李均力,郜丽静.2009.分步迭代的多光谱遥感水体信息高精度自动提取.遥感学报,13(4):610-615
Luo L and Mountrakis G.2010.Integrating intermediate inputs from partially classified images within a hybrid classification framework:An impervious surface estimation example.Remote Sensing of Environment,114(6):1220-1229[DOI:10.1016/j.rse.2010.01.008]
Mountrakis G,Watts R,Luo L R and Wang J D.2009.Developing collaborative classifiers using an expert-based model.Photogrammetric Engineering and Remote Sensing,75(7):831-843
Richards J A and Jia X P.2007.A dempster-shafer relaxation approach to context classification.IEEE Transactions on Geoscience and Remote Sensing,45(5):1422-1431[DOI:10.1109/TGRS.2007.893821]
Slonecker E T,Jennings D B and Garofalo D.2001.Remote sensing of impervious surfaces:A review.Remote Sensing Reviews,20(3):227-255[DOI:10.1080/02757250109532436]
Song C H,Woodcock C E,Seto K C,Lenney M P and Macomber S A.2001.Classification and change detection using landsat TM data:When and how to correct atmospheric effects?Remote Sensing of Environment,75(2):230-244[DOI:10.1016/S0034-4257(00)00169-3]
Weng Q H.2012.Remote sensing of impervious surfaces in the urban areas:Requirements,methods,and trends.Remote Sensing of Environment,117:34-49[DOI:10.1016/j.rse.2011.02.030]
Wu C S.2004.Normalized spectral mixture analysis for monitoring urban composition using ETM+imagery.Remote Sensing of Environment,93(4):480-492[DOI:10.1016/j.rse.2004.08.003]
Wu C S and Murray A T.2003.Estimating impervious surface distribution by spectral mixture analysis.Remote Sensing of Environment,84(4):493-505[DOI:10.1016/S0034-4257(02)00136-0]
Xian G G and Homer C.2010.Updating the 2001 national land cover database impervious surface products to 2006 using landsat imagery change detection methods.Remote Sensing of Environment,114(8):1676-1686[DOI:10.1016/j.rse.2010.02.018]
Yang L M,Huang C Q,Homer C G,Wylie B K and Coan M J.2003.An approach for mapping large-area impervious surfaces:synergistic use of Landsat-7 ETM+and high spatial resolution imagery.Canadian Journal of Remote Sensing,29(2):230-240[DOI:10.5589/m02-098]
周成虎,骆剑承,杨晓梅,杨存建,刘庆生.2000.遥感影像地学理解与分析.北京:科学出版社
Chang C C and Lin C J.2011.LIBSVM:A library for support vector ma-chines.ACM Transactions on Intelligent Systems and Technology,2(3):1-27[DOI:10.1145/1961189.1961199]
程熙,沈占锋,骆剑承,朱长明,周亚男,胡晓东.2011.利用混合光谱分解与SVM估算不透水面覆盖率.遥感学报,15(6):1235-1247
de Jong S M,Hornstra T and Maas H G.2001.An integrated spatial andspectral approach to the classification of Mediterranean land cover types:the SSC method.International Journal of Applied Earth O bservation and Geoinformation,3(2):176-183[DOI:10.1016/S0303-2434(01)85009-1]
Franke J,Roberts D A,Halligan K and Menz G.2009.Hierarchical Multiple Endmember Spectral Mixture Analysis(MESMA)of hyperspectral imagery for urban environments.Remote Sensing of Environment,113(8):1712-1723[DOI:10.1016/j.rse.2009.03.018]
Greenfield E J,Nowak D J and Walton J T.2009.Assessment of 2001NLCD Percent Tree and Impervious Cover Estimates,8 pp.,American Society for Photogrammetry and Remote Sensing,Bethesda,MD,ETATS-UNIS
Homer C,Dewitz J,Fry J,Coan M,Hossain N,Larson C,Herold N,McKerrow A,VanDriel J N and Wickham J.2007.Completion of the 2001 National Land Cover Database for the Conterminous United States.Photogrammetric Engineering and Remote Sensing,73(4):337-341
Kim K E.1996.Adaptive majority filtering for contextual classification of remote sensing data.International Journal of Remote Sensing,17(5):1083-1087[DOI:10.1080/01431169608949070]
骆剑承,盛永伟,沈占锋,李均力,郜丽静.2009.分步迭代的多光谱遥感水体信息高精度自动提取.遥感学报,13(4):610-615
Luo L and Mountrakis G.2010.Integrating intermediate inputs from partially classified images within a hybrid classification framework:An impervious surface estimation example.Remote Sensing of Environment,114(6):1220-1229[DOI:10.1016/j.rse.2010.01.008]
Mountrakis G,Watts R,Luo L R and Wang J D.2009.Developing collaborative classifiers using an expert-based model.Photogrammetric Engineering and Remote Sensing,75(7):831-843
Richards J A and Jia X P.2007.A dempster-shafer relaxation approach to context classification.IEEE Transactions on Geoscience and Remote Sensing,45(5):1422-1431[DOI:10.1109/TGRS.2007.893821]
Slonecker E T,Jennings D B and Garofalo D.2001.Remote sensing of impervious surfaces:A review.Remote Sensing Reviews,20(3):227-255[DOI:10.1080/02757250109532436]
Song C H,Woodcock C E,Seto K C,Lenney M P and Macomber S A.2001.Classification and change detection using landsat TM data:When and how to correct atmospheric effects?Remote Sensing of Environment,75(2):230-244[DOI:10.1016/S0034-4257(00)00169-3]
Weng Q H.2012.Remote sensing of impervious surfaces in the urban areas:Requirements,methods,and trends.Remote Sensing of Environment,117:34-49[DOI:10.1016/j.rse.2011.02.030]
Wu C S.2004.Normalized spectral mixture analysis for monitoring urban composition using ETM+imagery.Remote Sensing of Environment,93(4):480-492[DOI:10.1016/j.rse.2004.08.003]
Wu C S and Murray A T.2003.Estimating impervious surface distribution by spectral mixture analysis.Remote Sensing of Environment,84(4):493-505[DOI:10.1016/S0034-4257(02)00136-0]
Xian G G and Homer C.2010.Updating the 2001 national land cover database impervious surface products to 2006 using landsat imagery change detection methods.Remote Sensing of Environment,114(8):1676-1686[DOI:10.1016/j.rse.2010.02.018]
Yang L M,Huang C Q,Homer C G,Wylie B K and Coan M J.2003.An approach for mapping large-area impervious surfaces:synergistic use of Landsat-7 ETM+and high spatial resolution imagery.Canadian Journal of Remote Sensing,29(2):230-240[DOI:10.5589/m02-098]
周成虎,骆剑承,杨晓梅,杨存建,刘庆生.2000.遥感影像地学理解与分析.北京:科学出版社