Spatial and spectral preprocessor for spectral mixture analysis of synthetic remotely sensed hyperspectral image

Abstract

Linear combination of endmembers according to their abundance fractions at pixel level is as the result of low spatial resolution of hyperspectral sensors. Spectral unmixing problem is described by decomposing these medley pixels into a set of endmembers and their abundance fractions. Most of endmember extraction techniques are designed on the basis of spectral feature of images such as OSP. Also SSPP is implied which considers spatial content of image pixels besides spectral information. We propose a self-governing module prior the spectral based endmember extraction algorithms to achieve superior performance of RMSE and SAD-based errors by creating a new synthetic image using HYDRA tool and USGS library with various values of SNR in order to evaluate our method with OSP and SSPP+OSP. Experimental results in comparison with the mentioned methods show that the proposed method can unmix data more effectively.