Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing
Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing
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DOI：
10.1109/icip.2017.8296278
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
D. Hong;N. Yokoya;J. Chanussot;Xiaoxiang Zhu
中科院分区：
文献类型：
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作者：
D. Hong;N. Yokoya;J. Chanussot;Xiaoxiang Zhu
本文提出了一种新颖的光谱混合模型来解决高光谱分解逆问题中的光谱变异性。基于线性混合模型 (LMM)，我们的模型引入了光谱变异字典来解释 LMM 无法解释的任何残差。字典中的原子被认为与端元的光谱特征低相干。提出了一种字典学习技术来学习光谱变异字典，同时解决解混合问题。合成数据集和真实数据集的实验结果表明，所提出的方法的性能优于最先进的方法。
This paper presents a novel spectral mixture model to address spectral variability in inverse problems of hyperspectral unmixing. Based on the linear mixture model (LMM), our model introduces a spectral variability dictionary to account for any residuals that cannot be explained by the LMM. Atoms in the dictionary are assumed to be low-coherent with spectral signatures of endmembers. A dictionary learning technique is proposed to learn the spectral variability dictionary while solving unmixing problems simultaneously. Experimental results on synthetic and real datasets demonstrate that the performance of the proposed method is superior to state-of-the-art methods.