Joint Local Abundance Sparse Unmixing for Hyperspectral Images
Joint Local Abundance Sparse Unmixing for Hyperspectral Images
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DOI：
10.3390/rs9121224
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Mia Rizkinia;M. Okuda
中科院分区：
文献类型：
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作者：
Mia Rizkinia;M. Okuda
稀疏分解广泛用于高光谱图像，通过考虑丰度稀疏性来估计高光谱场景的混合像素（端元）中包含的材料的最佳分数（丰度）。这种丰度具有独特的性质，即局部区域具有较高的空间相关性。这是因为该区域存在的端元具有高度相关性。这意味着端元丰度的低等级。根据该先验知识，预计将低秩局部丰度考虑到稀疏解混合问题可以提高估计性能。在本研究中，我们提出了一种算法，通过将核范数应用于空间和丰度域局部区域的丰度矩阵来利用低秩局部丰度。在我们的优化问题中，局部丰度正则化器分别与稀疏性和空间信息的 L 2 , 1 范数和总变分配合。我们对真实和模拟的高光谱数据集进行了实验，假设存在和不存在纯像素。实验表明，我们的算法产生了有竞争力的结果，并且比传统算法表现得更好。
Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2 , 1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms.