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Superpixel nonlocal weighting joint sparse representation for hyperspectral image classification.

Zhang, Aizhu; Pan, Zhaojie; Fu, Hang; Sun, Genyun; Rong, Jun; Ren, Jinchang; Jia, Xiuping; Yao, Yanjuan


Aizhu Zhang

Zhaojie Pan

Hang Fu

Genyun Sun

Jun Rong

Jinchang Ren

Xiuping Jia

Yanjuan Yao


Joint sparse representation classification (JSRC) is a representative spectral–spatial classifier for hyperspectral images (HSIs). However, the JSRC is inappropriate for highly heterogeneous areas due to the spatial information being extracted from a fixed-sized neighborhood block, which is often unable to conform to the naturally irregular structure of land cover. To address this problem, a superpixel-based JSRC with nonlocal weighting, i.e., superpixel-based nonlocal weighted JSRC (SNLW-JSRC), is proposed in this paper. In SNLW-JSRC, the superpixel representation of an HSI is first constructed based on an entropy rate segmentation method. This strategy forms homogeneous neighborhoods with naturally irregular structures and alleviates the inclusion of pixels from different classes in the process of spatial information extraction. Afterwards, the superpixel-based nonlocal weighting (SNLW) scheme is built to weigh the superpixel based on its structural and spectral information. In this way, the weight of one specific neighboring pixel is determined by the local structural similarity between the neighboring pixel and the central test pixel. Then, the obtained local weights are used to generate the weighted mean data for each superpixel. Finally, JSRC is used to produce the superpixel-level classification. This speeds up the sparse representation and makes the spatial content more centralized and compact. To verify the proposed SNLW-JSRC method, we conducted experiments on four benchmark hyperspectral datasets, namely Indian Pines, Pavia University, Salinas, and DFC2013. The experimental results suggest that the SNLW-JSRC can achieve better classification results than the other four SRC-based algorithms and the classical support vector machine algorithm. Moreover, the SNLW-JSRC can also outperform the other SRC-based algorithms, even with a small number of training samples.


ZHANG, A., PAN, Z., FU, H., SUN, G., RONG, J., REN, J., JIA, X. and YAO, Y. 2022. Superpixel nonlocal weighting joint sparse representation for hyperspectral image classification. Remote sensing [online], 14(9), article 2125. Available from:

Journal Article Type Article
Acceptance Date Apr 27, 2022
Online Publication Date Apr 28, 2022
Publication Date May 1, 2022
Deposit Date May 2, 2022
Publicly Available Date May 2, 2022
Journal Remote sensing
Electronic ISSN 2072-4292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 9
Article Number 2125
Keywords Spatial–spectral fusion; Joint sparse representation classification (JSRC); Hyperspectral imaging; Superpixel; Nonlocal weighting
Public URL


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