Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs

Abstract

Spectral unmixing is one of the most popular techniques to analyze remotely sensed hyperspectral images. It generally comprises three stages: (1) reduction of the dimensionality of the original image to a proper subspace; (2) automatic identification of pure spectral signatures (called endmembers); and (3) estimation of the fractional abundance of each endmember in each pixel of the scene. The spectral unmixing process allows sub-pixel analysis of hyperspectral images, but can be computationally expensive due to the high dimensionality of the data. In this paper, we develop the first real-time implementation of a full spectral unmixing chain in commodity graphics processing units (GPUs). These hardware accelerators offer a source of computational power that is very appealing in hyperspectral remote sensing applications, mainly due to their low cost and adaptivity to on-board processing scenarios. The implementation has been developed using the compute device unified architecture (CUDA) and tested on an NVidia™ GTX 580 GPU, achieving real-time unmixing performance in two different case studies: (1) characterization of thermal hot spots in hyperspectral images collected by NASA's Airborne Visible Infra-red Imaging Spectrometer (AVIRIS) during the terrorist attack to the World Trade Center complex in New York City, and (2) sub-pixel mapping of minerals in AVIRIS hyperspectral data collected over the Cuprite mining district in Nevada.