Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity and Total Variation
Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity and Total Variation
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DOI：
10.1109/jstars.2016.2521898
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发表时间：
2016-09-01
影响因子：
5.5
通讯作者：
Majumdar, Angshul
中科院分区：
文献类型：
--
作者：
Aggarwal, Hemant Kumar;Majumdar, Angshul
高光谱分解是估计高光谱图像中每个像素上的组成端元及其分数丰度的过程。高光谱图像经常被多种噪声破坏。这项工作解决了考虑混合噪声存在的一般场景中的高光谱分解问题。分解模型明确考虑了高斯噪声和稀疏噪声。分解问题已被制定为利用丰度图的联合稀疏性。基于总变分的正则化也被用于对丰度图的平滑度进行建模。分裂布雷格曼技术已被用来导出解决由此产生的优化问题的算法。合成和真实高光谱图像的详细实验结果证明了所提出技术的优势。
Hyperspectral unmixing is the process of estimating constituent endmembers and their fractional abundances present at each pixel in a hyperspectral image. A hyperspectral image is often corrupted by several kinds of noise. This work addresses the hyperspectral unmixing problem in a general scenario that considers the presence of mixed noise. The unmixing model explicitly takes into account both Gaussian noise and sparse noise. The unmixing problem has been formulated to exploit joint-sparsity of abundance maps. A total-variation-based regularization has also been utilized for modeling smoothness of abundance maps. The split-Bregman technique has been utilized to derive an algorithm for solving resulting optimization problem. Detailed experimental results on both synthetic and real hyperspectral images demonstrate the advantages of proposed technique.