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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Vydané v:	International journal of parallel programming Ročník 47; číslo 2; s. 272 - 292
Hlavní autori:	López-Fandiño, Javier, B. Heras, Dora, Argüello, Francisco, Dalla Mura, Mauro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2019 Springer Nature B.V Springer Verlag
Predmet:	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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Abstract	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.
AbstractList	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\[\times \] with respect to an OpenMP implementation. 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.
Author	Dalla Mura, Mauro Argüello, Francisco B. Heras, Dora López-Fandiño, Javier
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Keywords	Hyperspectral change detection CUDA Segmentation Change Vector Analysis GPU Spectral Angle Mapper
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Snippet	Nowadays, it is increasingly common to detect land cover changes using remote sensing multispectral images captured at different time-frames over the same...
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SubjectTerms	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
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Title	GPU Framework for Change Detection in Multitemporal Hyperspectral Images
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