基于独立分量分析的高光谱遥感图像混合像元盲分解

Blind unmixing based on independent component analysis for hyperspectral imagery

传统的独立分量分析并不适用于高光谱遥感图像的混合像元解混,因为图像中各端元的分布不是相互独立的.针对这一问题,提出了一种有约束的独立分量分析方法,来实现遥感图像混合像元的盲分解.通过在独立分量分析的目标函数中引入丰度非负约束与丰度和为一约束,改变了传统的独立性假设.同时,为了更好地适用于遥感数据分析,还提出了一种自适应的丰度建模方法来描述数据的概率分布,对各种不同的遥感数据进行建模.仿真数据和真实高光谱数据的实验结果表明,作为一种无需光谱先验信息的算法,具有更高的分解精度,为高光谱遥感图像混合像元的盲分解提供了一种有效的解决手段.

In hyperspectral unmixing,endmember signals are not independent with each other,which compromise the application of independent component analysis（ICA） algorithm.This paper presented a novel approach based on constrained ICA for hyperspectral unmixing to overcome this problem.By introducing the constraints of abundance nonnegative and abundance sum-to-one,the purpose of our algorithm was not to find independent components as decomposition results anymore.In order to accord with the condition of hyperspectral imagery,we developed an abundance modeling technique to describe the statistical distribution of the data.The modeling approach is capable of self-adaptation,and can be applied to hyperspectral images with different characteristics.Experimental results on both simulated and real hyperspectral data demonstrated that the proposed approach can obtain more accurate results than the other state-of-the-art approaches.As an algorithm with no need of spectral prior knowledge,our method provided an effective technique for the blind unmixing of hyperspectral imagery.