NDLI: Region-division-based joint sparse representation classification for hyperspectral images

Content Provider	IET Digital Library
Author	Yan, Jingwen Chen, Hongda Zhai, Yikui Liu, Yinan Liu, Lei
Abstract	In this article, a region-division-based joint sparse representation classification (RDJSRC) method is proposed to solve the heterogeneous region problem in the joint sparse representation classification (JSRC) method used in hyperspectral image (HSI) classification. The RDJSRC method incorporates regional information, obtained by the hidden Markov random field (HMRF), into the JSRC to reduce the interference of heterogeneous pixels in the neighbourhood of the test pixel and finally improve the classification performance. The framework of this method is as follows. The first several principal components (PCs) are initially selected to be the new HSI by transforming the original HSI with the PC analysis algorithm. Then, the regional information containing the spatial structure of the HSI is obtained by applying the HMRF algorithm to the first PC. Through incorporating this regional information into the JSRC procedure, the initial label of the test pixel can be jointly determined by the new HSI pixels within the homogeneity in the search window. Ultimately, the final label of the test pixel is determined by a voting strategy based on multiple classification results. Compared with several classification methods, experimental results, indicate that this method achieves improvement from 2 to 3% in HSI classification.
Starting Page	1694
Ending Page	1704
Page Count	11
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6667
Journal	IET Image Processing
Publisher Date	2019-05-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Method Classification Performance Computer Vision And Image Processing Technique Feature Extraction Geophysical Image Processing Heterogeneous Pixel Heterogeneous Region Problem Hidden Markov Random Field HSI Classification HSI Pixel Hyperspectral Image Hyperspectral Image Classification Image Classification Image Representation Image Segmentation Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research JSRC Markov Processes Multiple Classification Result Optical, Image And Video Signal Processing Original HSI RDJSRC Method Region-division-based Joint Sparse Representation Classification Method Regional Information Statistics Test Pixel
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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