Ahmad Keshavarz

Spectral Mixture Analysis is one of the fundamental subjects encountered when dealing with remotely sensed hyperspectral images. Its goal is to identify constituent elements of mixed-pixels called Endmembers (EMs) and their associated abundance maps. In this paper, a novel Geodesic and Euclidean distances-based preprocessing (GEPP) is addressed which is coupled with the classical spectral-based EM Extraction algorithms (EEs). It combines both spatial and spectral information utilizing two approaches with the purpose of searching for spectrally pure and spatially homogenous pixels that may be identified as the EM candidates in subsequent EEs. GEPP reduces EE processing time by introducing a new correlation coefficient similarity function () on the spectrally pure and spatially homogenous pixels pick up with the help of spectral weighting computations, unsupervised Fuzzy C-means (FCM) clustering algorithm and a spatial neighbourhood system using Markov Random Field (MRF) so that processing a large amount of mixed and heterogeneous pixels developed by EEs is avoided. Moreover, exploits the spatial Euclidean and novel spectral Geodesic weights to compute the final mean vector which is able to improve recognition of spatially homogenous regions that are highly spectrally correlated such that it leads to better results of unmixing accuracy. According to experimental results on three synthetic and four real hyperspectral scenes, hyperspectral unmixing outcomes are relatively improved in terms of SAD and RMSE-based error metrics and higher computation speed can be realized by our proposal in comparison with the state-of-the-art techniques.