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

作     者：DU Pei-jun TAN Kun SU Hong-jun 

作者机构：Department of Remote Sensing and Geographical Information Science China Xuzhou Jiangsu 221008 China Key Laboratory for Virtual Geographic Environment of Ministry of Education Nanjing Normal University Nanjing 

出 版 物：《Mining Science and Technology》 (矿业科学技术（英文版）)

年 卷 期：2009年第19卷第6期

页      面：835-841页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

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 

主　　题：hyperspectral remote sensing feature extraction decision tree SVM OMIS 

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.