CONSTRAINED INDEPENDENT COMPONENT ANALYSIS FOR HYPERSPECTRAL UNMIXING

In hyperspectral unmixing, endmember signals are not independent with each other, restricting the application of independent component analysis (ICA). We present a new algorithm to overcome this problem. By introducing abundance nonnegative and abundance sum-to-one constraints into objective function of ICA, the goal of our method is changed from "independence" to "uncorrelation". We also develop an abundance modeling technique to describe the statistical distribution of hyperspectral data. The modeling approach is capable of self-adaptation, and can be applied to various images with different characteristics. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed approach can obtain accurate results. As an algorithm with no need of spectral prior knowledge, our method provides an effective technique for hyperspectral unmixing.