现代信息科技2025，Vol.9Issue(5)：45-50,6.DOI:10.19850/j.cnki.2096-4706.2025.05.008
基于L1/2稀疏性和峰度平滑约束非负矩阵分解的高光谱图像解混
HU Based on L1/2 Sparsity and Kurtosis Smoothing Constrained Non-negative Matrix Factorization
摘要
Abstract
In order to solve the problems existing in traditional HU methods,such as low unmixing efficiency,complex computation and vulnerability to noise and outliers,an algorithm based on L1/2-KSNMF is proposed.Aiming at nonlinear mixing situation in HSI,this method first introduces the L1/2 norm as a measure of sparsity to improve the accuracy of unmixing.By introducing kurtosis smoothing constraint,the spatial information is fused into the unmixing model to enhance the spatial continuity of the unmixing results.The experimental results show that this algorithm demonstrates excellent performance in terms of unmixing accuracy,computational efficiency,as well as the extraction of endmember spectra from hyperspectral data.关键词
高光谱图像/非负矩阵分解/L1/2稀疏约束/高光谱图像解混(HU)Key words
HSI/Non-negative Matrix Factorization/L1/2 sparse constraint/HU分类
计算机与自动化引用本文复制引用
杨国亮,张佳琦,盛杨杨..基于L1/2稀疏性和峰度平滑约束非负矩阵分解的高光谱图像解混[J].现代信息科技,2025,9(5):45-50,6.基金项目
江西省教育厅科技计划项目(GJJ210861) （GJJ210861）
江西省教育厅科技项目(GJJ200879) （GJJ200879）
