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| hiperespectral:cva [2016/10/10 11:24] – [Downloads] javier.lopez.fandino | hiperespectral:cva [2018/01/17 13:52] (actual) – javier.lopez.fandino |
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| Experimental results related to the paper GPU-based Change Detection Applied to Multitemporal Agricultural Hyperspectral Images. | ===== GPU Framework for Change Detection of Multitemporal Hyperspectral Images ===== |
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| | Experimental results related to the paper [[https://link.springer.com/article/10.1007/s10766-017-0547-5|GPU Framework for Change Detection of Multitemporal Hyperspectral Images]] published in the International Journal of Parallel Programming. |
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| ==== Abstract ==== | ==== Abstract ==== |
| Nowadays, it is increasingly common to detect land cover changes through remote sensing multidimensional images captured at different timeframes. A great part of the available change detection methods focus on pixel-based operations. The use of spectral-spatial techniques helps to improve the results but increases the processing times. In this work, a GPU framework to find object-based differences in multitemporal remote sensing hyperspectral data is presented. The method is based on Change Vector Analysis (CVA) and involves different segmentation algorithms to take into account the spatial information of the images. The Spectral Angle Mapper (SAM) distance and Otsu's thresholding are also used. The projection in GPU allows change detection applications to be efficiently computed in real-time. The method is applied to agricultural hyperspectral images obtaining a speedup of 16.1x against the 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 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 GPU (Graphical Processor Unit) framework to perform object-based change detection in multitemporal remote sensing hyperspectral data is presented. It is based on Change Vector Analysis (CVA) with the Spectral Angle Mapper (SAM) 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. |
| ===== Downloads ===== | ===== Downloads ===== |
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| == Input datasets == | === Input dataset === |
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| //For information see the readme in the files.// | //All the images are avaiable in Matlab (.mat) format, among others. For further information see the readme in the files.// |
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| * [[https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset|Santa Barbara]] | * [[https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset|Santa Barbara]] |
| * [[https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset|Bay Area]] | * [[https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset|Bay Area]] |
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| | === Results === |
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| == Results == | == Experimental conditions == |
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| //For information see the readme in the files.// | //For information see the readme in the files.// |