Cluster-based Spatial Border Removal Preprocessor for improvement of endmember extraction in real remotely sensed hyperspectral image

Endmember extraction (EE) is defined as the process of identifying a set of pure and unique spectral signatures of the materials combined at pixel level in a remotely sensed hyperspectral scene. These extracted signatures are utilized to decompose scene into abundance fractions by means of Spectral Mixture Analysis (SMA) model Most of algorithms used for EE, are established on spectral discrimination without any attention to spatial context and distribution of image pixels. Some years ago several algorithms are implied which applies spatial context in addition to spectral discrimination in a distinct module such as SPP and SSPP. In this paper, we propose a novel Cluster-based Spatial Border Removal Preprocessor (CSBRP) by removing spatial borders of cluster map and calculate spectral purity weight of residual pixels in order to look for spatially homogenous and spectrally pure pixels with help of spectral purity weight threshold in a distinct module. The purpose of CSBRP is to improve RMSE reconstruction of original image and a new metric defined as Efficiency by reducing the processing time needed for EE. Experimental results on the proposed preprocessor outperform the state of the art EE methods, SPP and SSPP in real Aviris hyperspectral Salinas and Indian Pines images.