Feature extraction is a key operation for hyperspectral image(HSI)classification.For current classification approaches,they usually ignore the information preservation and spatial distribution in feature extraction,which may export features with low information utilization and disordered distribution,generating unsatisfactory prediction results.To remedy such deficiencies,a novel method based on structure-wise feature reconstruction is proposed for the HSI classification.This method can reduce the information loss and improve the information preservation during the process of feature extraction.In addition,the distribution is also fully considered to enhance the discriminability and separability.In this proposed meth-od,considering the reconstruction idea and the self-expression theory,a structure-wise feature reconstruction model is con-structed to extract the features of the HSI,which can improve the information utilization of original information from the HSI and describe the structure reflecting the well-ordered distribution.Here,an optimization with alternative updating is pre-sented to solve the above constructed model.The support vector machine is finally used to classify the extracted features and predict the labels of the HSI.The Salinas,Pavia Center,Botswana,and Houston datasets are used for experimental vali-dation.Results show that the proposed method achieves the better classification performance compared with some state-of-the-art approaches,which is averagely higher 2.6%,3.9%,3.3%at OA(Overall Accuracy),AA(Average Accuracy),and Kappa indexes.
维普
万方数据