GPU Framework for Change Detection in Multitemporal Hyperspectral Images

Nowadays, it is increasingly common to detect land cover changes using remote sensing multispectral images captured at different time-frames over the same area. A large part of the available change detection (CD) methods focus on pixel-based operations. The use of spectral–spatial techniques helps t...

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Vydáno v:International journal of parallel programming Ročník 47; číslo 2; s. 272 - 292
Hlavní autoři: López-Fandiño, Javier, B. Heras, Dora, Argüello, Francisco, Dalla Mura, Mauro
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2019
Springer Nature B.V
Springer Verlag
Témata:
Change detection
Computer programming
Computer Science
Engineering Sciences
Graphics processing units
Hyperspectral imaging
Image detection
Image segmentation
Land cover
Microprocessors
Parallel processing
Processor Architectures
Remote sensing
Signal and Image processing
Software Engineering/Programming and Operating Systems
Spatial data
Special Issue on High-Level Parallel Programming
Theory of Computation
Vector analysis
Watersheds
Hyperspectral change detection
CUDA
Segmentation
Change Vector Analysis
GPU
Spectral Angle Mapper
ISSN:0885-7458, 1573-7640
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Shrnutí:Nowadays, it is increasingly common to detect land cover changes using remote sensing multispectral images captured at different time-frames over the same area. A large part of the available change detection (CD) methods focus on pixel-based operations. The use of spectral–spatial techniques helps to improve the accuracy results but also implies a significant increase in processing time. In this paper, a Graphic Processor Unit (GPU) framework to perform object-based CD in multitemporal remote sensing hyperspectral data is presented. It is based on Change Vector Analysis with the Spectral Angle Mapper distance and Otsu’s thresholding. Spatial information is taken into account by considering watershed segmentation. The GPU implementation achieves real-time execution and speedups of up to 46.5 × with respect to an OpenMP implementation.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-017-0547-5