基于L_(1/2)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 L_(1/2)Sparsity and Kurtosis Smoothing Constrained Non-negative Matrix Factorization

作　　者：杨国亮 张佳琦 盛杨杨 YANG Guoliang;ZHANG Jiaqi;SHENG Yangyang(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《现代信息科技》2025年第5期45-50,共6页Modern Information Technology

基　　金：江西省教育厅科技计划项目(GJJ210861);江西省教育厅科技项目(GJJ200879)。

摘　　要：为了解决传统高光谱图像解混方法中存在的解混效率低、计算复杂和易受噪声和异常点影响等问题,提出了一种基于L_(1/2)稀疏性和峰度平滑约束非负矩阵分解(L_(1/2)-KSNMF)的算法。针对高光谱图像中非线性混合情形,该方法首先引入了L_(1/2)范数作为稀疏度度量,提高解混的准确性;引入峰度平滑约束,将空间信息融合到解混模型中,提高解混结果的空间连续性;实验结果表明,该算法在解混准确性和计算效率以及从高光谱数据中提取端元光谱方面都表现出优异的性能。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 L_(1/2)-KSNMF is proposed.Aiming at nonlinear mixing situation in HSI,this method first introduces the L_(1/2)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 L_(1/2)稀疏约束 高光谱图像解混(HU) 

分 类 号：TP751.2[自动化与计算机技术—检测技术与自动化装置]