A Novel Approach Based on Fisher Discriminant Null Space for Decomposition of Mixed Pixels in Hyperspectral Imagery
A Novel Approach Based on Fisher Discriminant Null Space for Decomposition of Mixed Pixels in Hyperspectral Imagery
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DOI：
10.1109/lgrs.2010.2046134
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发表时间：
2010-05
影响因子：
4.8
通讯作者：
Jing Jin;Bin Wang-;Liming Zhang
中科院分区：
文献类型：
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作者：
Jing Jin;Bin Wang-;Liming Zhang
传统的光谱混合分析假设每个端元必须具有恒定的光谱特征。然而，端元光谱变异性在实际应用中往往存在，这会降低混合像元分解的精度。为了解决这一问题，本文提出了一种基于Fisher判别零空间（FDNS）的高光谱图像混合像元分解方法。FDNS搜索光谱的线性变换，这使得那些端元光谱在每个端元组内没有变化，但在不同端元组之间有很大的差异。因此，利用变换后的光谱可以在很大程度上降低端元光谱变异性对解混精度的负面影响。仿真和真实的高光谱图像的实验结果表明，该算法对高光谱图像混合像元的分解具有较高的精度。
Traditional spectral mixture analysis assumes that each endmember must have a constant spectral signature. However, endmember spectral variability always exists in practical situations, which results in reducing the accuracy of the decomposition of mixed pixels. In order to solve this problem, this letter proposes a new method based on Fisher discriminant null space (FDNS) for decomposition of mixed pixels in hyperspectral imagery. The FDNS searches a linear transformation of the spectra, which makes those endmember spectra to have no variability inside each endmember group but large differences among different endmember groups. Therefore, the negative impact caused by endmember spectral variability on unmixing accuracy can be decreased to a large extent by using the transformed spectra. Experimental results of both simulated and real hyperspectral images demonstrate that the proposed algorithm has a high accuracy for the decomposition of mixed pixels in hyperspectral imagery.