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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Adaptive Spatial Regularization Sparse Unmixing Strategy Based on Joint MAP for Hyperspectral Remote Sensing Imagery
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Adaptive Spatial Regularization Sparse Unmixing Strategy Based on Joint MAP for Hyperspectral Remote Sensing Imagery

机译：基于联合MAP的高光谱遥感影像自适应空间正则稀疏分解策略

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摘要

Sparse unmixing, as a recently developed spectral unmixing approach, has been successfully applied based on the assumption that the observed image signatures can be expressed in an efficient linear sparse regression with the potentially very large endmember spectral library. To improve the unmixing accuracy, spatial information has been incorporated in the sparse unmixing formulation by adding an appropriate spatial regularization for the hyperspectral remote sensing imagery. However, for the traditional spatial regularization sparse unmixing (SRSU) algorithms, it is a difficult task to set appropriate user-defined regularization parameters in real applications, and this often has a high computational cost. To overcome the difficulty of the regularization parameter selection, the adaptive spatial regularization sparse unmixing (ASRSU) strategy based on the joint maximum a posteriori (JMAP) estimation technique is proposed in this paper. In ASRSU, the SRSU problem is formulated in the framework of JMAP with an appropriate prior model. ASRSU considers the regularization parameters and the abundances jointly by an alternating iterative process, and the relationships between the regularization parameters and the abundances are obtained from the JMAP model. Based on the ASRSU strategy, two ASRSU algorithms are presented: the adaptive total variation spatial regularization sparse unmixing algorithm and the adaptive nonlocal means filtering sparse unmixing algorithm. The experimental results demonstrate that the two proposed ASRSU algorithms based on JMAP can adaptively obtain optimal or near-optimal regularization parameters for the three simulated datasets and the two real hyperspectral remote sensing images.
机译：稀疏分解是一种新近开发的光谱分解方法，它基于这样的假设，即所观察到的图像签名可以通过潜在的非常大的端成员光谱库以有效的线性稀疏回归表示，这一假设已得到成功应用。为了提高解混精度，通过为高光谱遥感影像添加适当的空间正则化，将空间信息纳入稀疏解混公式中。但是，对于传统的空间正则化稀疏分解（SRSU）算法，在实际应用中设置合适的用户定义正则化参数是一项艰巨的任务，并且这通常具有很高的计算成本。为了克服正则化参数选择的难点，提出了一种基于联合最大后验（JMAP）估计技术的自适应空间正则化稀疏混合（ASRSU）策略。在ASRSU中，SRSU问题是在JMAP框架中用适当的先验模型提出的。 ASRSU通过交替的迭代过程共同考虑正则化参数和丰度，并且从JMAP模型获得了正则化参数和丰度之间的关系。基于ASRSU策略，提出了两种ASRSU算法：自适应总变化空间正则化稀疏分解算法和自适应非局部均值滤波稀疏分解算法。实验结果表明，所提出的两种基于JMAP的ASRSU算法可以自适应地获得三个模拟数据集和两个真实的高光谱遥感图像的最优或接近最优的正则化参数。

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