Classification of hyperspectral remote sensing images using frequency spectrum similarity

作     者：WANG Ke GU XingFa YU Tao MENG QingYan ZHAO LiMin FENG Li 

作者机构：Institute of Remote Sensing ApplicationsChinese Academy of SciencesState Key Laboratory of Remote Sensing Science Institute of Regional Development PlanningUniversity of Stuttgart 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2013年第56卷第4期

页      面：980-988页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081002[工学-信号与信息处理] 

基　　金：supported by the National Basic Research Program of China ("973" Program) (Grant No. 2010CB950800) International S&T Cooperation Program of China (Grant No. 2010DFA21880) China Postdoctoral Science Foundation (Grant No. 2012M510053) 

主　　题：hyperspectral image spectral similarity frequency spectrum feature remote sensing classification 

摘      要：An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral *** spectral signature of each pixel in the hyperspectral image is taken as a discrete signal,and the frequency spectrum is obtained using discrete Fourier *** discrepancy of frequency spectrum between ground objects spectral signatures is visible,thus the difference between frequency spectra of reference and target spectral signature is used to measure the spectral *** distance is introduced to increase the contribution from higher frequency ***,the number of harmonics involved in the proposed algorithm is determined after analyzing the frequency spectrum energy cumulative distribution function of ground *** order to evaluate the performance of the proposed algorithm,two hyperspectral remote sensing images are adopted as experimental *** proposed algorithm is compared with spectral angle mapper (SAM),spectral information divergence (SID) and Euclidean distance (ED) using the product accuracy,user accuracy,overall accuracy,average accuracy and Kappa *** results show that the proposed algorithm can be applied to hyperspectral image classification effectively.