FPGA implementation of the pixel purity index algorithm for remotely sensed hyperspectral image analysis

Authors:
Carlos González;Javier Resano;Daniel Mozos;Antonio Plaza;David Valencia
Affiliations:
Departament of Computer Architecture and Automatics, Computer Science Faculty, Complutense University of Madrid, Madrid, Spain;Departament of Computer Architecture, High Polytechnic Center, University of Zaragoza, Zaragoza, Spain;Departament of Computer Architecture and Automatics, Computer Science Faculty, Complutense University of Madrid, Madrid, Spain;Departament of Computer Technology and Communications, Polytechnic School of Cáceres, University of Extremadura, Cáceres, Spain;Departament of Computer Technology and Communications, Polytechnic School of Cáceres, University of Extremadura, Cáceres, Spain
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

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Abstract

Hyperspectral imaging is a new emerging technology in remote sensing which generates hundreds of images, at different wavelength channels, for the same area on the surface of the Earth. Over the last years, many algorithms have been developed with the purpose of finding endmembers, assumed to be pure spectral signatures in remotely sensed hyperspectral data sets. One of the most popular techniques has been the pixel purity index (PPI). This algorithm is very time-consuming. The reconfigurability, compact size, and high computational power of Field programmable gate arrays (FPGAs) make them particularly attractive for exploitation in remote sensing applications with (near) real-time requirements. In this paper, we present an FPGA design for implementation of the PPI algorithm. Our systolic array design includes a DMA and implements a prefetching technique to reduce the penalties due to the I/O communications. We have also included a hardware module for random number generation. The proposed method has been tested using real hyperspectral data collected by NASA's Airborne Visible Infrared Imaging Spectrometer over the Cuprite mining district in Nevada. Experimental results reveal that the proposed hardware system is easily scalable and able to provide accurate results with compact size in (near) real-time, whichmake our reconfigurable system appealing for on-board hyperspectral data processing.