GPU Implementation of Spatial–Spectral Preprocessing for Hyperspectral Unmixing

Abstract

Spectral unmixing pursues the identification of spectrally pure constituents, called endmembers , and their corresponding abundances in each pixel of a hyperspectral image. Most unmixing techniques have focused on the exploitation of spectral information alone. Recently, some techniques have been developed to take advantage of the complementary information provided by the spatial correlation of the pixels in the image. Computational complexity represents a major problem in these spatial–spectral techniques, as hyperspectral images contain very rich information in both the spatial and spectral domains. In this letter, we develop a computationally efficient implementation of a spatial–spectral processing algorithm that has been successfully applied prior to the spectral unmixing of the hyperspectral data. Our implementation has been optimized for the commodity graphics processing units (GPUs) and is evaluated (using both synthetic and real data) using different GPU architectures. Significant speedups can be achieved when processing hyperspectral images of different sizes. This allows for the inclusion of the proposed parallel preprocessing module in a full hyperspectral unmixing chain able to operate in real time.