Performance-Power Evaluation of an OpenCL Implementation of the Simplex Growing Algorithm for Hyperspectral Unmixing

Abstract

Over the last few years, several new strategies for spectral unmixing of remotely sensed hyperspectral data have been proposed. Many of them have been developed to solve the most time-consuming and relevant step: endmember extraction. However, unmixing algorithms can be computationally very expensive in terms of processing time and energy consumption, a fact that compromises their use in applications under real-time and energy/power constraints. In this letter, we present a new parallel simplex growing algorithm (SGA) for hyperspectral data which exploits the memory hierarchy with operations in single-precision floating point. Those optimizations accelerate the most time-consuming parts of this method using the open computing language (OpenCL) standard. We have evaluated the performance versus energy consumption using the same open standard for parallel programming over a diverse set of heterogeneous platforms. Experiments have been conducted using real hyperspectral images collected by NASA’s Airborne Visible Infrared Imaging Spectrometer and a collection of 24 synthetic hyperspectral images simulated with different sizes and number of endmembers (10–30). Considering the power consumption and OpenCL across all the proposed devices, the analysis presented indicates that the SGA can now be executed in computationally efficient fashion, which was not possible before introducing the parallel implementation described in this letter.