A novel spatial spectral Preprocessor for improvement of hyperspectral unmixing

The purpose of unmixing in hyperspectral images is extraction of the endmembers spectral signatures and estimation of their related abundance fractions. Most algorithms used for endmember extraction (EE) process, are established on spectral information without any attention to spatial context and correlation of image pixels. Recently, several algorithms have been developed which utilize spatial and spectral information with the aim of improving EE and unmixing accuracy. In this paper, a novel spatial spectral preprocessor is proposed which exploits class map obtained by unsupervised clustering technique and 8th neighborhood window in order to identify pixels located in border regions between two or more clusters and discards not spatially homogenous regions. Afterwards, it calculates spectral purity weight of not border pixels in order to look for spatially homogenous and spectrally pure ones using otsu threshold. Endmembers can be extracted rapidly and accurately by means of coupling our proposal with EEs. Our distinct scheme can reduce RMSE of reconstructed image and EE processing time as well as improve a new criterion known as Efficiency regarding the state-of-the-art preprocessors on real hyperspectral images.