利用融合纹理与形态特征进行地震倒塌房屋信息自动提取
|
摘要
提出了一种以震后单一时相高空间分辨率光学遥感影像为基础,融合纹理特征和形态特征的地震倒塌房屋自动提取方法,研究了不同尺度纹理特征和形态特征在倒塌房屋提取中的作用和表现。以5.12汶川地震作为研究实例,结果表明,本方法能够有效提取地震倒塌房屋。倒塌房屋产品精度和用户精度分别为86.65%和86.35%,Kappa系数为0.790 6。
Extracting damaged buildings accurately and quickly through remote sensing has an important meaning to damage evaluation and relief after earthquake.A method,which based only on high spatial resolution optical remotely sensed image acquired after earthquake and fused textural features and morphological features,was proposed to extract damaged buildings automatically.Besides,the use and behaving of different scales textural features and morphological features in damaged buildings extraction were investigated.A study case,Wenchuan Earthquake,was studied.Experiment results showed that this method could extract damaged buildings effectively and reach a good result that the product accuracy and the user accuracy of damaged buildings,and Kappa coefficient were 86.65%,86.35% and 0.790 6 respectively.
Extracting damaged buildings accurately and quickly through remote sensing has an important meaning to damage evaluation and relief after earthquake.A method,which based only on high spatial resolution optical remotely sensed image acquired after earthquake and fused textural features and morphological features,was proposed to extract damaged buildings automatically.Besides,the use and behaving of different scales textural features and morphological features in damaged buildings extraction were investigated.A study case,Wenchuan Earthquake,was studied.Experiment results showed that this method could extract damaged buildings effectively and reach a good result that the product accuracy and the user accuracy of damaged buildings,and Kappa coefficient were 86.65%,86.35% and 0.790 6 respectively.
引文
[1]Chini M,Pierdicca N,Emery W J.Exploiting SARand VHR Optical Images to Quantify DamageCaused by the 2003 Bam Earthquake[J].IEEETransactions on Geoscience and Remote Sensing,2009,47(1):145-152
[2]Haralick R M,Shanmugam K,Dinstein I.TexturalFeatures for Image Classification[J].IEEE Trans-actions on Systems,Man,and Cybernetics,1973,3(6):610-621
[3]Clausi D A.An Analysis of Co-occurrence TextureStatistics as a Function of Grey Level Quantization[J].Canadian Journal of Remote Sensing,2002,28(1):45-62
[4]Coburn C A,Roberts A C B.A Multiscale TextureAnalysis Procedure for Improved Forest Stand Clas-sification[J].International Journal of Remote Sens-ing,2004,25(20):4 287-4 308
[5]Pesaresi M.Texture Analysis for Urban PatternRecognition Using Fine Resolution PanchromaticSatellite Imagery[J].Geographical and Environ-mental Modeling,2000,4(1):47-67
[6]Clausi D A,Zhao Y.Grey Level Co-occurrence In-tegrated Algorithm(GLCIA):a Superior Computa-tional Method to Rapidly Determine Co-occurrenceProbability Texture Features[J].Computers&Ge-osciences,2003,29(7):837-850
[7]Pesaresi M,Benediktsson J A.A New Approachfor the Morphological Segmentation of High-resolu-tion Satellite Imagery[J].IEEE Transactions onGeoscience and Remote Sensing,2001,39(2):309-320
[8]Guyon I,Weston J,Barnhill S.Gene Selection forCancer Classification Using Support Vector Ma-chines[J].Machine Learning,2002,46(1):389-422
[9]Vapnik V N.The Nature of Statistical LearningTheory[M].New York:Springer-Verlag,1995
[10]Chang C C,Lin C J.LIBSVM:a Library for Sup-port Vector Machines[OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm,2009
[2]Haralick R M,Shanmugam K,Dinstein I.TexturalFeatures for Image Classification[J].IEEE Trans-actions on Systems,Man,and Cybernetics,1973,3(6):610-621
[3]Clausi D A.An Analysis of Co-occurrence TextureStatistics as a Function of Grey Level Quantization[J].Canadian Journal of Remote Sensing,2002,28(1):45-62
[4]Coburn C A,Roberts A C B.A Multiscale TextureAnalysis Procedure for Improved Forest Stand Clas-sification[J].International Journal of Remote Sens-ing,2004,25(20):4 287-4 308
[5]Pesaresi M.Texture Analysis for Urban PatternRecognition Using Fine Resolution PanchromaticSatellite Imagery[J].Geographical and Environ-mental Modeling,2000,4(1):47-67
[6]Clausi D A,Zhao Y.Grey Level Co-occurrence In-tegrated Algorithm(GLCIA):a Superior Computa-tional Method to Rapidly Determine Co-occurrenceProbability Texture Features[J].Computers&Ge-osciences,2003,29(7):837-850
[7]Pesaresi M,Benediktsson J A.A New Approachfor the Morphological Segmentation of High-resolu-tion Satellite Imagery[J].IEEE Transactions onGeoscience and Remote Sensing,2001,39(2):309-320
[8]Guyon I,Weston J,Barnhill S.Gene Selection forCancer Classification Using Support Vector Ma-chines[J].Machine Learning,2002,46(1):389-422
[9]Vapnik V N.The Nature of Statistical LearningTheory[M].New York:Springer-Verlag,1995
[10]Chang C C,Lin C J.LIBSVM:a Library for Sup-port Vector Machines[OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm,2009
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心