Dimensionality reduction of hyperspectral images based on subspace combination clustering and adaptive band selection

Chunsen Zhang; Hengheng Liu

doi:10.1117/12.2539312

14 February 2020 Dimensionality reduction of hyperspectral images based on subspace combination clustering and adaptive band selection

Chunsen Zhang, Hengheng Liu

Author Affiliations +

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320F (2020) https://doi.org/10.1117/12.2539312
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

This paper proposes a method of hyperspectral image dimensionality reduction based on automatic subspace partition, k-means clustering based on mutual information and adaptive band selection. This method first automatic subspace division method is used to determine the initial subspace, in various initial subspace through the mutual information between image variance and band and K - means to determine the clustering center and clustering center from two adjacent band selection and their mutual information between the difference between the absolute minimum band as a boundary to delimit the molecular space, and then in the subspace of division of each band is obtained by applying the method of adaptive band selection index, get the biggest index of each subspace of band and from big to small order according to the index, at last in the first three band is the selection of bands. OMIS hyperspectral data were used to conduct experiments, and this method has a higher classification accuracy than the previous band selection methods.

Citation Download Citation

Chunsen Zhang and Hengheng Liu "Dimensionality reduction of hyperspectral images based on subspace combination clustering and adaptive band selection", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320F (14 February 2020); https://doi.org/10.1117/12.2539312

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