The Study on Blind Unmixing for Hyperspectral Imagery

Hyperspectral is the frontiers of Remote Sensing development, which plays a more and more important role in many fields. the mixed pixels become an main obstacle to the in depth development for quantification imagery analysis,This paper presented a novel approach based on independent component analysis for hyperspectral unmixing,it 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. It 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 demonstrated that the proposed approach can obtain more accurate results than the other state-of-the-art approaches.