通过稀疏与总变差约束提升高光谱解混精度
Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery
- 多层矩阵分解结合总变差正则化，利用空间相邻相似性
- 采用L1/2范数刻画丰度矩阵稀疏性，提升解混准确性
- 适合需要高精度解混的遥感图像处理场景
高光谱解混旨在估计物质端元及其对应的丰度比例，是多种高光谱影像应用中的关键预处理步骤。本文提出一种新型方法——稀疏与总变差约束的多层线性解混（STVMLU）。基于多层矩阵分解模型，引入总变差约束以考虑邻近空间相似性，并采用L1/2-范数稀疏约束有效刻画丰度矩阵的稀疏特性。为优化该模型，使用交替方向乘子法（ADMM）实现端元及其丰度矩阵的联合提取。实验结果表明，所提方法在性能上优于其他算法。
原文摘要 · Abstract (English)
Hyperspectral unmixing aims at estimating material signatures (known as endmembers) and the corresponding proportions (referred to abundances), which is a critical preprocessing step in various hyperspectral imagery applications. This study develops a novel approach called sparsity and total variation (TV) constrained multilayer linear unmixing (STVMLU) for hyperspectral imagery. Specifically, based on a multilayer matrix factorization model, to improve the accuracy of unmixing, a TV constraint is incorporated to consider adjacent spatial similarity. Additionally, a L1/2-norm sparse constraint is adopted to effectively characterize the sparsity of the abundance matrix. For optimizing the STVMLU model, the method of alternating direction method of multipliers (ADMM) is employed, which allows for the simultaneous extraction of endmembers and their corresponding abundance matrix. Experimental results illustrate the enhanced performance of the proposed STVMLU when compared to other algorithms.
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