Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

作     者：SOOMRO Bushra Naz XIAO Liang SOOMRO Shahzad Hyder MOLAEI Mohsen SOOMRO Bushra Naz;肖亮;SOOMRO Shahzad Hyder;MOLAEI Mohsen

作者机构：School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China 

出 版 物：《Journal of Donghua University(English Edition)》 (东华大学学报（英文版）)

年 卷 期：2016年第33卷第6期

页      面：954-960页

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：National Key Research and Development Program of China(No.2016YFF0103604) National Natural Science Foundations of China(Nos.61171165,11431015,61571230) National Scientific Equipment Developing Project of China(No.2012YQ050250) Natural Science Foundation of Jiangsu Province,China(No.BK20161500) 

主　　题：learning algorithms hyper-spectral image classification support vector machine(SVM) multinomial logistic regression(MLR) elastic net regression(ELNR) sparse representation(SR) spatial-aware 

摘      要：A comprehensive assessment of the *** mpervised learning algorithms for *** image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating *** context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.