FPGA Implementation of Endmember Extraction Algorithms from Hyperspectral Imagery: Pixel Purity Index versus N-FINDR Carlos Gonz´ alez a , Daniel Mozos a , Javier Resano b and Antonio Plaza c a Department of Computer Architecture and Automatics, Complutense University of Madrid, C/ Profesor Jos´ e Garc´ ıa Santesmases s/n 28040 Madrid (Spain) b Department of Computer Architecture, University of Zaragoza, C/ de Mar´ ıa de Luna 3, 50018 Zaragoza (Spain) c Department of Technology of Computers and Communications, University of Extremadura, Avda. de la Universidad s/n E-10071 C´ aceres (Spain) ABSTRACT 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 (abun- dance) 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 re- motely sensed hyperspectral data and provide implementations that can significantly outperform the (optimized) equivalent software versions of the considered endmember extraction algorithms. Keywords: Hyperspectral image analysis, endmember extraction, pixel purity index (PPI), N-FINDR, field programmable gate arrays (FPGAs). 1. INTRODUCTION Hyperspectral imaging, also known as imaging spectroscopy, is a technique that has been widely used during recent years in Earth and planetary remote sensing. 1 It generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. The concept of hyperspectral imaging originated at NASA’s Jet Propulsion Laboratory in California, which developed instruments such as the Airborne Imaging Spectrometer (AIS), then called AVIRIS (for Airborne Visible Infra-Red Imaging Spectrometer 2 ). This system is now able to cover the wavelength region from 400 to 2500 nanometers using 224 spectral channels, at nominal spectral resolution of 10 nanometers. As a result, each pixel (considered as a vector) collected by a hyperspectral instrument can be seen as a spectral signature or ‘fingerprint’ of the underlying materials within the pixel (see Figure 1). Further author information: (Send correspondence to Carlos Gonz´ alez) Carlos Gonz´alez: carlosgonzalez@fdi.ucm.es, Daniel Mozos: mozos@fis.ucm.es, Javier Resano: jresano@unizar.es, Antonio Plaza: aplaza@unex.es High-Performance Computing in Remote Sensing, edited by Bormin Huang, Antonio J. Plaza, Proc. of SPIE Vol. 8183, 81830F · © 2011 SPIE · CCC code: 0277-786X/11/$18 · doi: 10.1117/12.897384 Proc. of SPIE Vol. 8183 81830F-1