Efficient coarse-to-fine spectral rectification for hyperspectral image.

Compared to studies of analyzing hyperspectral image (HSI), finding spectral rectification, especially the seriously distorted pixels in boundaries, was seldom addressed in a clear way, albeit of first importance in HSI analysis and interpretation. In this paper, we present a simple but promising rectification method works in a coarse-to-fine framework for removing noise and enhancing useful features. Our approach, called CSR (coarse-to-fine spectral rectification), combines the theory of scale-aware with local smoothness for HSI rectification problem that is seldom pointed out. The useless information like noise in small scale is removed firstly. Then, the distinctive information like boundary in large scale is enhanced. The experimental result enjoys a built-in smoothing effect and a fact of the identical materials with same or similar signatures, which is suited for HSI subsequent application. Furthermore, our approach has powerful influence on both classification via five classifiers in terms of class labeled data and unmixing regarding to class unlabeled data. Rectified by a coarse-to-fine framework, our method presents superior performance and runs much faster than the competing methods. [ABSTRACT FROM AUTHOR]