An Automatic Robust Iteratively Reweighted Unstructured Detector for Hyperspectral Imagery

Abstract

Unstructured background model-based detectors, which usually utilize a single a priori target spectral signature as the input, have been successfully applied in various hyperspectral target detection applications. However, the detection results are greatly affected by the quality of the a priori target spectral signature, as the spectral variability phenomenon is universal in hyperspectral image data. This paper proposes an iteratively reweighted method to generate an optimal target spectrum from limited target training spectra, which is able to alleviate the spectral variation. When the priori target spectral can only be chosen from the hyperspectral image, an optimal target spectrum can be iteratively generated by adjusting the pixel signals with varying weights. The experimental results with three different types of real hyperspectral images confirm the robust performance of the proposed method.