Diferenzas
Isto amosa as diferenzas entre a revisión seleccionada e a versión actual da páxina.
| Both sides previous revisionPrevious revisionNext revision | Previous revision |
| hiperespectral:cva [2016/09/14 17:13] – [Downloads] javier.lopez.fandino | hiperespectral:cva [2018/01/17 13:52] (actual) – javier.lopez.fandino |
|---|
| 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 ===== |
| | |
| | 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. |
| | |
| ==== Abstract ==== | ==== Abstract ==== |
| Nowadays, it is increasingly common to have access to several multidimensional remote sensing images captured in the same region at different timeframes which allows the study of the changes over these areas. The biggest part of the available change detection techniques are focused on pixel-based operations. In this work, a binary spectral-spatial change detection technique to find object-based differences in multitemporal remote sensing hyperspectral data is presented. The technique is based on Change Vector Analysis (CVA) improved with Spectral Angle Mapper (SAM) distance and Otsu's thresholding. It also involves a initial watershed segmentation-based technique to take into account the spatial information of the images. The technique can be efficiently executed in real-time applications thanks to its projection to GPU devices. | |
| | 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 ===== |
| |
| == Input datasets == | === Input dataset === |
| //For information see the readme files in the archives.// | |
| | //All the images are avaiable in Matlab (.mat) format, among others. For further information see the readme in the files.// |
| | |
| | * [[https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset|Santa Barbara]] |
| |
| * Barbara | * [[https://citius.usc.es/investigacion/datasets/hyperspectral-change-detection-dataset|Bay Area]] |
| |
| * Bay Area | === Results === |
| |
| | == Experimental conditions == |
| |
| == Results == | //For information see the readme in the files.// |
| //For information see the readme file in the archives.// | |
| |
| *{{:hiperespectral:outputscva.zip|}} | * {{:hiperespectral:outputscva.zip|}} |
| |
| ===== License ===== | ===== License ===== |
| |
| :cc-by-nc-nd: | :cc-by-nc-nd: |