Отрывок: Here we pro- vide experimental results for Indian Pines scene, which was acquired using AVIRIS sensor (some results for the Salinas hyperspectral scene are present in the Appendix). Indian Pines image contains 145×145 pixels in 224 spec- tral bands. Only 200 bands were selected by removing bands with the high level of noise and water absorption. This hyperspectral scene is provided with the groundtruth segmentation mask that is ...
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Поле DC | Значение | Язык |
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dc.contributor.author | Myasnikov, E.V. | - |
dc.date.accessioned | 2017-10-25 12:07:20 | - |
dc.date.available | 2017-10-25 12:07:20 | - |
dc.date.issued | 2017-08 | - |
dc.identifier | Dspace\SGAU\20171020\65763 | ru |
dc.identifier.citation | Myasnikov EV. Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches. Computer Optics 2017; 41(4): 564-572 | ru |
dc.identifier.uri | https://dx.doi.org/10.18287/2412-6179-2017-41-4-564-572 | - |
dc.identifier.uri | http://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Hyperspectral-image-segmentation-using-dimensionality-reduction-and-classical-segmentation-approaches-65763 | - |
dc.description.abstract | Unsupervised segmentation of hyperspectral satellite images is a challenging task due to the nature of such images. In this paper, we address this task using the following three-step procedure. First, we reduce the dimensionality of the hyperspectral images. Then, we apply one of classical segmentation algorithms (segmentation via clustering, region growing, or watershed transform). Finally, to overcome the problem of over-segmentation, we use a region merging procedure based on priority queues. To find the parameters of the algorithms and to compare the segmentation approaches, we use known measures of the segmentation quality (global consistency error and rand index) and well-known hyperspectral images. | ru |
dc.description.sponsorship | The reported study was funded by the Russian Foundation for Basic Research (RFBR) grants 16-29-09494 ofi_m and 16-37-00202 mol_a. | ru |
dc.language.iso | en | ru |
dc.publisher | Самарский университет | ru |
dc.relation.ispartofseries | 41;4 | - |
dc.subject | hyperspectral image | ru |
dc.subject | segmentation | ru |
dc.subject | clustering | ru |
dc.subject | watershed transform | ru |
dc.subject | region growing | ru |
dc.subject | region merging | ru |
dc.subject | segmentation quality measure | ru |
dc.subject | global consistency error | ru |
dc.subject | rand index | ru |
dc.title | Hyperspectral image segmentation using dimensionality reduction and classical segmentation approaches | ru |
dc.type | Article | ru |
dc.textpart | Here we pro- vide experimental results for Indian Pines scene, which was acquired using AVIRIS sensor (some results for the Salinas hyperspectral scene are present in the Appendix). Indian Pines image contains 145×145 pixels in 224 spec- tral bands. Only 200 bands were selected by removing bands with the high level of noise and water absorption. This hyperspectral scene is provided with the groundtruth segmentation mask that is ... | - |
dc.classindex.scsti | 29.31.15 | - |
dc.classindex.scsti | 29.33.43 | - |
dc.classindex.scsti | 20.53.23 | - |
Располагается в коллекциях: | Журнал "Компьютерная оптика" |
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410415.pdf | 1.89 MB | Adobe PDF | Просмотреть/Открыть |
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