GPU implementation of an automatic target detection and classification algorithm for hyperspectral image analysis

IEEE Geoscience and Remote Sensing Letters

Volumn 10, Issue 2, 2013, Pages 221-225

  Bernabe, Sergio ^a   Lopez, Sebastián ^b   Plaza, Antonio ^a   Sarmiento, Roberto ^b  

^a UNIVERSITY OF EXTREMADURA   (Spain)

^b UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

Author keywords

Automatic target detection and classification algorithm (ATDCA); commodity graphics processing units (GPUs); Gram Schmidt (GS) orthogonalization; hyperspectral imaging

Indexed keywords

ACCELERATION FACTORS; AUTOMATIC TARGET DETECTION AND CLASSIFICATION ALGORITHMS; CLASSIC ALGORITHM; COMPUTATIONAL PERFORMANCE; COMPUTATIONAL POWER; COMPUTING PERFORMANCE; DETECTION ACCURACY; FLOATING POINTS; GPU IMPLEMENTATION; GRAM-SCHMIDT ORTHOGONALIZATIONS; GRAPHICS PROCESSING UNITS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGE ANALYSIS; HYPERSPECTRAL IMAGERY; HYPERSPECTRAL IMAGING; LOW LEVEL; ORTHOGONAL PROJECTION; ORTHOGONALIZATION; REAL TIME PERFORMANCE; REAL-TIME RESPONSE; SHARED MEMORIES; SPEED-UP FACTORS;

COMPUTER GRAPHICS; COMPUTER GRAPHICS EQUIPMENT; INDEPENDENT COMPONENT ANALYSIS; MEMORY ARCHITECTURE; OPTIMIZATION; PROGRAM PROCESSORS; REMOTE SENSING; SPECTROSCOPY; TARGET TRACKING;

ALGORITHMS;

EID: 84869502405     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2012.2198790     Document Type: Article

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