doi:10.16186/j.cnki.1673-9787.2020.4.7

Received:2019-11-28 00:00:00

Revised:2020-01-30 00:00:00

Published:2020/07/15

Feature extraction of hyperspectral image based on multilinear sparseprincipal components

CHEN Zhichao1, ZHANG Zheng1, LIU Changhua1, ZHOU Yawen2, LU Junjun1, WANG Chunyang1

1.College of Surveying and Land Information Engineering， Henan Polytechnic University， Jiaozuo  454000 ， Henan， China;2.Beijing GEOWAY Software Co. ， Ltd. ， Beijing  100194 ， China

Abstract:The feature extraction of hyperspectral image helps to improve the application efficiency and accuracy of hyperspectral data. Considering the disadvantage that vector based feature extraction algorithm could not make full use of the cube spatial structure information of hyperspectral image， the multilinear sparse principal component analysis（ MSPCA） algorithm was proposed to perform sparse dimensionality reduction in all tensor modes. Based on the hyperspectral images of typical villages in Jiaxing， China， and the currite mining area in Nevada， USA， three feature extraction methods i. e. ， principal component analysis （ PCA ） ， spatial principal component analysis （ SPCA ） and multi-linear discriminant analysis （ MPCA ） ， were used to compare and analyze the classification accuracy of the proposed algorithm after feature extraction. The results showed that the classification accuracy of feature extraction obtained by MSPCA was better than that of the other three methods， and the overall classification accuracy of the proposed algorithm in the two experimental areas was 96. 36% and 95.00% respectively.

Key words:hyperspectral image;multilinear sparse principal component analysis;feature extraction

基于多线性稀疏主成分的高光谱影像特征提取_陈志超.pdf