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| hiperespectral:eadp_gpu [2019/04/17 12:56] – [Indian Pines EADP experimental results] alvaro.accion | hiperespectral:eadp_gpu [2020/07/20 17:26] (actual) – [Extended Anisotropic Diffusion Profiles in GPU for Hyperspectral Imagery] alvaro.accion | ||
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| Experimental results related to the paper Extended Anisotropic Diffusion Profiles in GPU for Hyperspectral Imagery by Álvaro Acción, Francisco Argüello, and Dora B. Heras. | Experimental results related to the paper Extended Anisotropic Diffusion Profiles in GPU for Hyperspectral Imagery by Álvaro Acción, Francisco Argüello, and Dora B. Heras. | ||
| + | https:// | ||
| ===== Abstract ===== | ===== Abstract ===== | ||
| Morphological profiles are common approach for extracting spatial information from hyperspectral images by extracting structural features. Additional kinds of profiles can be built based on different approaches as, for example, differential morphological profiles, or attribute profiles. Another technique used for characterizing spatial information on the images at different scales is based on computing edge-preserving filters such as anisotropic diffusion filters. Their main advantage is to preserve the distinctive morphological features of the images at a the cost of an iterative calculation. In this paper, the high computational cost associated to the construction of Anisotropic Diffusion Profiles (ADPs) is drastically reduced with the usage of GPUs. In particular, we propose a low cost computational approach for computing ADPs in Nvidia GPUs as well as a detailed characterization of the method, comparing it in terms of accuracy and structural similarity to other existing alternatives. | Morphological profiles are common approach for extracting spatial information from hyperspectral images by extracting structural features. Additional kinds of profiles can be built based on different approaches as, for example, differential morphological profiles, or attribute profiles. Another technique used for characterizing spatial information on the images at different scales is based on computing edge-preserving filters such as anisotropic diffusion filters. Their main advantage is to preserve the distinctive morphological features of the images at a the cost of an iterative calculation. In this paper, the high computational cost associated to the construction of Anisotropic Diffusion Profiles (ADPs) is drastically reduced with the usage of GPUs. In particular, we propose a low cost computational approach for computing ADPs in Nvidia GPUs as well as a detailed characterization of the method, comparing it in terms of accuracy and structural similarity to other existing alternatives. | ||
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| The average results for 100 classification experiments using the EADP are as follows: | The average results for 100 classification experiments using the EADP are as follows: | ||
| - | ^ Metric ^ Value ^ | + | ^ Metric ^ Value ^ Std ^ |
| - | | OA | 92.45% | | + | | OA | 92.45% |
| - | | AA | 95.27% | | + | | AA | 95.27% |
| - | | Kappa | 91.34% | | + | | Kappa | 91.34% |
| - | ^ Class ^ Accuracy ^ | + | ^ # ^ Class |
| - | | C1 | 0.981 | | + | | 1 | Alfalfa |
| - | | C2 | 0.928 | | + | | 2 | Corn-notill |
| - | | C3 | 0.959 | | + | | 3 | Corn-mintill |
| - | | C4 | 0.987 | | + | | 4 | Corn | 0.987 |
| - | | C5 | 0.967 | | + | | 5 | Grass/ |
| - | | C6 | 0.989 | | + | | 6 | Grass-trees |
| - | | C7 | 0.961 | | + | | 7 | Grass-pasture-mowed |
| - | | C8 | 1 | + | | 8 | Hay-windrowed |
| - | | C9 | 1 | + | | 9 | Oats | 1 |
| - | | C10 | + | | 10 | Soybean-notill |
| - | | C11 | + | | 11 | Soybean-mintill |
| - | | C12 | + | | 12 | Soybean-clean |
| - | | C13 | + | | 13 | Wheat |
| - | | C14 | + | | 14 | Woods |
| - | | C15 | + | | 15 | Bld-Grass-Trees |
| - | | C16 | + | | 16 | Stone-Steel |