Speeding up the MATLAB™ Hyperspectral Image Analysis Toolbox using GPUs and the Jacket Toolbox

• Published in: 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing pp. 1 - 4
• Main Authors: Rosario-Torres, S., Velez-Reyes, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2009
• Subjects: Application software; classification; Classification algorithms; CUDA; Data mining; Feature extraction; GPU; Hyperspectral Image Processing; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Jacket toolbox; MATLAB; Multispectral imaging; Software tools; solutionware
• ISBN: 9781424446865; 1424446864
• ISSN: 2158-6268
• DOI: 10.1109/WHISPERS.2009.5289089

The Hyperspectral Image Analysis Toolbox (HIAT) is a MATLAB trade toolbox for the analysis of hyperspectral imagery. HIAT includes a collection of algorithms for processing of hyperspectral and multispectral imagery under the MATLAB environment. The objective of HIAT is to provide a suite of information extraction algorithms to users of hyperspectral and multispectral imagery across different application domains. HIAT has been developed as part of the NSF Bernard M. Gordon Center for Subsurface Sensing and Imaging Solutionware that seeks to develop a repository of reliable and reusable software tools that can be shared by researchers across research domains. HIAT includes feature extraction and selection, supervised and unsupervised classification algorithms, unmixing, and visualization algorithms developed at the UPRM Laboratory for Applied Remote Sensing and Image Processing. A key limitation of the MATLAB environment is its difficulty in managing large images. Here we investigate the use of the recently released MATLAB Jacket Toolbox that allows implementation of MATLAB programs in GPUs. This paper presents a comparison of the CPU implementation with the GPU implementation of different routines of HIAT.