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《Arid Land Geography》 2006-01
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Feature selection and extraction of hyperspectral data — Based on HyMap data of Barrax in Spanish

CHEN Gui-hong1,TANG Ling-li2,JIANG Xiao-guang2(1 Beijing Information Resources Management Center,Beijing 100088;China;2 China Remote Sensing Satellite Ground Station,Chinese Academy of Sciences,Beijing 100086,China)  
In this paper,a case study on the feature selection and extraction of the hyperspectral data of HyMap(128 bands) in Barrax,Spanish,is carried out based on K-L transform,segment K-L transform,wavelet transform,band index and some separability functions so as to select the optimal band combinations of the spectra of land covers.The original HyMap data were derived from Barrax test site in Spanish at 9:00 o’clock,June 4,1999,the air altitude was 3 500 m,and the spatial resolving power was about 5 m.the spectral range of HyMap airborne imaging spectrometer was 400~2 480 nm,there were 128 wavebands except two with high vapor absorption near 1.4 μm and 1.9 μm.The wavebands can be divided into 4 groups,i.e.,the visible light,near infrared rays,shortwave infrared ray 1 and shortwave infrared ray 2,each waveband group includes 32 wavebands,and the waveband width is about 16 nm.The atmospheric correction and geometry correction of the test data are carried out,and the values of reflectivity of the ground objects are normalized.The classification accuracies of different methods of the feature selection and extraction are compared and analyzed based on the common supervised classification methods,such as the maximum likelihood,parallelepiped and minimum distance.The results show that the classification accuracy with feature selection based on band indexes and average dispersibility is high,and that with feature extraction based on K-L transform,segment K-L transform and wavelet transform is high.The compared and analyzed results show that the classification accuracy of the parallelepiped is relatively low,that of minimum distance is relatively high,and that of the maximum likelihood stable.
【Fund】: 国家自然科学基金项目“水环境指标的高光谱响应模型研究”(40271048);; 863项目(2002AA130010-1-4)
【CateGory Index】: P237
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