Multi-view hyperspectral image classification via weighted sparse representation

被引：0
作者：
Zhao Y. [1 ]
Qin Y. [2 ]
Li Z. [3 ]
Huang W. [3 ]
Hou R. [1 ]
机构：
[1] Shandong Police College, Ji’nan
[2] Northwest Institute of Nuclear Technology, Xi’an
[3] Hubei University, Wuhan
来源：
Multimedia Tools and Applications | 2024年 / 83卷 / 42期
基金：
中国国家自然科学基金;
关键词：
Hyperspectral image classification; Multi-view data; Renyi entropy; Sparse representation;
D O I：
10.1007/s11042-024-18917-2
中图分类号：
学科分类号：
摘要：
Hyperspectral image classification aims to classify pixels in hyperspectral image into different land-cover classes. Considering multi-view data can improve the classification performance as it contains more abundant information than single view, a multi-view based method is proposed for hyperspectral image classification in this paper. In the proposed method, spatial neighboring pixels are adaptively determined by the mean and deviation of samples in the fixed-size spatial window. Then, the sparse representation algorithm is realized to obtain the sparse coefficients for the multi-view matrix set of the spatial neighboring pixels. Moreover, the renyi-entropy is calculated to determine the weight of each view in the classification task since the performance of different views is different. Finally, the obtained sparse coefficients and weights are jointly used to determine the class label of each test pixel. Experimental results on the Indian Pines dataset and University of Pavia dataset demonstrate the superior performance of the proposed method compared to several state-of-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
引用
收藏
页码：90207 / 90226
页数：19
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