MISC

2019年4月

GPU Framework for Change Detection in Multitemporal Hyperspectral Images

International Journal of Parallel Programming
  • Javier López-Fandiño
  • ,
  • Dora B. Heras
  • ,
  • Francisco Argüello
  • ,
  • Mauro Dalla Mura

47
2
開始ページ
272
終了ページ
292
DOI
10.1007/s10766-017-0547-5

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. 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.

リンク情報
DOI
https://doi.org/10.1007/s10766-017-0547-5
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038124277&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85038124277&origin=inward

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