Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity
Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity
复制标题
使用高斯混合模型和空间群稀疏性进行高光谱分解
DOI：
10.3390/rs11202434
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Xiaoguang Mei
中科院分区：
文献类型：
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作者：
Qiwen Jin;Yong Ma;Erting Pan;Fan Fan;Jun Huang;Hao Li;Chenhong Sui;Xiaoguang Mei
近年来，端元变率在高光谱解混领域受到了广泛的关注。为了解决端元签名不准确所带来的问题，通常对端元进行建模，假设其服从统计分布。然而，这些基于分布的方法仅使用光谱信息，并没有充分利用可能的局部空间相关性。当像素位于非均匀区域时，相邻像素的丰度将不共享相同的先验约束。因此，为了获得更好的丰度估计性能，本文提出了一种基于高斯混合模型（GMM）和空间组稀疏约束的方法。为了充分利用组结构，我们采用超像素分割（SS）作为预处理来生成空间组。然后，我们使用GMM模型的端元分布，将空间组稀疏作为一个混合范数正则化的目标函数。最后，在贝叶斯框架下，条件密度函数导致一个标准的最大后验概率（MAP）问题，可以使用广义期望最大化（GEM）来解决。对模拟和真实的高光谱数据的实验表明，该算法具有较高的混合解混精度。
In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical distribution. However, those distribution-based methods only use the spectral information alone and do not fully exploit the possible local spatial correlation. When the pixels lie on the inhomogeneous region, the abundances of the neighboring pixels will not share the same prior constraints. Thus, in this paper, to achieve better abundance estimation performance, a method based on the Gaussian mixture model (GMM) and spatial group sparsity constraint is proposed. To fully exploit the group structure, we take the superpixel segmentation (SS) as preprocessing to generate the spatial groups. Then, we use GMM to model the endmember distribution, incorporating the spatial group sparsity as a mixed-norm regularization into the objective function. Finally, under the Bayesian framework, the conditional density function leads to a standard maximum a posteriori (MAP) problem, which can be solved using generalized expectation-maximization (GEM). Experiments on simulated and real hyperspectral data demonstrate that the proposed algorithm has higher unmixing precision compared with other state-of-the-art methods.