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Experimental results related to the paper “GPU Framework for Change Detection of Multitemporal Agricultural Hyperspectral Images” under revision to be published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
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 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
Input datasets
All the images are avaiable in Matlab (.mat) format, among others. For further information see the readme in the files.
* Bay Area
Results
For information see the readme in the files.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.