Fast spatial-spectral preprocessing for endmember extraction and spectral unmixing using graphic processing units

Abstract

Linear spectral unmixing consists on the identification of spectrally pure constituents, called endmembers and their corresponding proportions or abundances using a linear model. Traditionally, most of the attention has been focussed on the exploitation of spectral information when identifying a set of endmembers and, only recently, some techniques try to take advantage of complementary information such as the one provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in most of these spatial-spectral based techniques, as hyperspectral images provide very rich information in both the spatial and the spectral domain. In this paper we provide a computationally efficient implementation of a spatial-spectral processing (SSPP) algorithm which can be used prior to endmember identification and spectral unmixing. Specifically we present an implementation optimized for commodity graphics processing units (GPUs), which is evaluated using two different GPU architectures from NVidia: GeForce GTX580 and GeForce GT740. Our experimental validation reveals that significant speedups can be achieved when processing hyperspectral images of different sizes.