Feature extraction for target identification and image classification of OMIS hyperspectral image  

作　　者：DU Pei-jun TAN Kun SU Hong-jun 

机构地区：[1]Department of Remote Sensing and Geographical Information Science, China Xuzhou, Jiangsu 221008, China [2]Key Laboratory for Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing,

出　　处：《Mining Science and Technology》2009年第6期835-841,共7页矿业科学技术（英文版）

基　　金：Projects 40401038 and 40871195 supported by the National Natural Science Foundation of China;NCET-06-0476 by the Program for New Century Excellent Talents in University;20070290516 by the Specialized Research Fund for the Doctoral Program of Higher Education

摘　　要：In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index (SAI),continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine (SVM) classifier with different feature extraction strategies including principal component analysis (PCA),minimum noise fraction (MNF),grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS (operational modular imaging spectrometer) image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.In order to combine feature extraction operations with specific hyperspectral remote sensing information processing objectives,two aspects of feature extraction were explored. Based on clustering and decision tree algorithm,spectral absorption index （SAI）,continuum-removal and derivative spectral analysis were employed to discover characterized spectral features of different targets,and decision trees for identifying a specific class and discriminating different classes were generated. By combining support vector machine （SVM） classifier with different feature extraction strategies including principal component analysis （PCA）,minimum noise fraction （MNF）,grouping PCA,and derivate spectral analysis,the performance of feature extraction approaches in classification was evaluated. The results show that feature extraction by PCA and derivate spectral analysis are effective to OMIS （operational modular imaging spectrometer） image classification using SVM,and SVM outperforms traditional SAM and MLC classifiers for OMIS data.

关 键 词：hyperspectral remote sensing feature extraction decision tree SVM OMIS 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]