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《Science of Surveying and Mapping》 2014-11
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Hyperspectral remote sensing image classification based on improved OIF and SVM algorithm

ZHANG Lei;SHAO Zhen-feng;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University;  
A novel method was developed to classify hyperspectral remote sensing images based on improved Optimum Index Factor(OIF)and Support Vector Machine(SVM)algorithm in the paper.First,a steady coefficient was proposed to select initial bands.Second,some bands according to the correlation between the remaining bands and the initial bands were selected to produce the new image.Third,the OIF was calculated for the new image,and found that the best band combination was with the maximum OIF value.Then,the best band combination was classified by SVM with "One-Against-One"classification strategy.Finally,the comparison between the accuracies of the classification and other supervised classifications was carried out.The experimental results indicated that the optimal classification bands combination could be effectively obtained with higher accuracy of classification by SVM algorithm.
【Fund】: 国家973计划重点项目(2010CB731801);; 国家自然科学基金项目(61172174);; 重大科技专项(2012YQ16018505 2013BAH42F03);; 国家海洋局数字海洋科学技术重点实验室开放基金资助(KLDO201307)
【CateGory Index】: TP751
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