FPGA Implementation of Endmember Extraction Algorithms from Hyperspectral Imagery: Pixel Purity Index versus N-FINDR

Endmember extraction is an important task for remotely sensed hyperspectral data exploitation. It comprises the identification of spectral signatures corresponding to macroscopically pure components in the scene, so that mixed pixels (resulting from limited spatial resolution, mixing phenomena happening at different scales, etc.) can be decomposed into combinations of pure component spectra weighted by an estimation of the proportion (abundance) of each endmember in the pixel. Over the last years, several algorithms have been proposed for automatic extraction of endmembers from hyperspectral images. These algorithms can be time-consuming (particularly for high-dimensional hyperspectral images). Parallel computing architectures have offered an attractive solution for fast endmember extraction from hyperspectral data sets, but these systems are expensive and difficult to adapt to on-board data processing scenarios, in which low-weight and low-power hardware components are essential to reduce mission payload, overcome downlink bandwidth limitations in the transmission of the hyperspectral data to ground stations on Earth, and obtain analysis results in (near) real-time. In this paper, we perform an inter-comparison of the hardware implementations of two widely used techniques for automatic endmember extraction from remotely sensed hyperspectral images: the pixel purity index (PPI) and the N-FINDR. The hardware versions have been developed in field programmable gate arrays (FPGAs). Our study reveals that these reconfigurable hardware devices can bridge the gap towards on-board processing of remotely sensed hyperspectral data and provide implementations that can significantly outperform the (optimized) equivalent software versions of the considered endmember extraction algorithms.