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Collaborative Sparse Unmixing of Hyperspectral Data Considering the Difference of Endmembers
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摘要: 因现有的高光谱协同稀疏解混模型忽略了不同像元所包含端元的差异性,影响到丰度估计的准确性。该文提出一种先对具有相同端元的像元进行无监督聚类的预处理,然后对预处理后的不同类高光谱像元进行协同稀疏解混算法。在无监督聚类过程中,由于具有相同原子集合的像元之间的协同稀疏编码值最小,将重构误差与协同稀疏编码约束之和作为距离测度,从而有效保证了同类像元中具有相同端元;再利用基于ADMM的优化算法对每类像元分别进行协同稀疏解混。仿真和实际高光谱数据实验结果表明,该算法能有效地进行真实端元识别,从而提高了丰度估计的准确性。Abstract: In the current collaborative sparse unmixing of hyperspectral data, the fractional abundances can not be estimated accurately due to ignoring the differences of endmembers among different pixels. In this paper, a novel unsupervised clustering method is proposed as a preprocessing step to generate several classes of pixels with the same endmember bundles, and then for each class, the collaborative sparse unmixing technique is used to implement spectral unmixing. In terms that the pixels with the same set of active atoms have the smallest values of collaborative sparse coding, the sum of reconstruction errors and sparsity levels are introduced as the distance metric in the unsupervised clustering. As such, the same class pixels can be guaranteed to contain the same endmembers. Finally, the involving optimization problem can be solved by using the algorithm of alternating direction method of multipliers (ADMM). Experimental results on synthetic and real hyperspectral data demonstrate that the our proposed algorithm can identify the actual endmembers effectively and improve the accuracy of the fractional abundance estimation.
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