Noise-robust spatial preprocessing prior to endmember extraction from hyperspectral data

This paper develops a noise-robust spatial preprocessing module which can be used prior to spectral unmixing of remotely sensed hyperspectral images. The method first derives a spatial homogeneity index which is relatively insensitive to the noise present in the original hyperspectral data. Then, it fuses this index with a spectral-based classification, obtaining a set of pure regions which are used to guide the unmixing process. An experimental comparison of the proposed method with other spatial-spectral unmixing approaches is conducted using both synthetic and real hyperspectral data collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experiments indicate that spectral unmixing can benefit from the proposed pre-processing approach, in particular, when the noise level present in the original hypespectral scene is relatively high.