Graph regularized reweighted sparsity constrained deep nonnegative matrix factorization for hyperspectral image unmixing
JIANG Yongcong¹, HE Fei²
1. Department of information and art design, Henan Forestry Vocational College, Henan Luoyang 471002, China;
2. School of Information and Engineering, Zhengzhou University, Zhengzhou, 450001, China
Abstract: In order to effectively exploit the hidden layer information of hyperspectral images and improve the decomposition accuracy of mixed pixels, this paper proposes a graph regularized reweighted sparse deep nonnegative matrix factorization algorithm. The algorithm respectively considers the graph structure information and sparsity properties of the abundance matrix from local and global perspectives. It improves the decomposition ability of mixed pixels by fusing the graph and the reweighted sparse constraints and the multi-layer deep structure of nonnegative matrix. The algorithm is solved layer by layer by multiplication update rules for the purpose of optimizing the global framework. Based on spectral angle distance and root mean square error metrics, experiments on simulated and real data sets show that the proposed algorithm has the largest unmixing accuracy gain of about 63% and 9.7%, respectively, compared with other representative algorithms. Experiments indicate that the proposed algorithm can effectively improve the interpretation accuracy of hyperspectral images and serve the major national needs.
Keywords: hyperspectral image;graph laplacian;reweighted sparsity;deep nonnegative matrix factorization;hyperspectral image unmixing.
2022, 48(12):154-161,180  收稿日期: 2021-10-06;收到修改稿日期: 2022-01-21
基金项目: 国家自然科学基金资助项目(61572444)；河南省青年骨干教师项目(2019GZGG023)
作者简介: 蒋永丛(1983-),男,讲师,主研方向为计算机应用、教育信息化
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