GPU Framework for Change Detection in Multitemporal Hyperspectral Images

• Published in: International journal of parallel programming Vol. 47; no. 2; pp. 272 - 292
• Main Authors: López-Fandiño, Javier, B. Heras, Dora, Argüello, Francisco, Dalla Mura, Mauro
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-04-2019; Springer Nature B.V; Springer Verlag
• Subjects: 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
• DOI: 10.1007/s10766-017-0547-5

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
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with respect to an OpenMP implementation.