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Article: An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery
Title | An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery |
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Authors | |
Issue Date | 2005 |
Citation | International Journal of Remote Sensing, 2005, v. 26, n. 22, p. 5149-5159 How to Cite? |
Abstract | Texture analysis of remote sensing images based on classification of area units represented in image segments is usually more accurate than operating on an individual pixel basis. In this paper we suggest a two-step procedure to segment texture patterns in remotely sensed data. An image is first classified based on texture analysis using a multi-parameter and multi-scale technique. The intermediate results are then treated as initial segments for subsequent segmentation based on the Gaussian Markov random field (GMRF) model. The segmentation procedure seeks to merge pairs of segments with the minimum variance difference. Experiments using real data prove that the two-step procedure improves both computational efficiency and accuracy of texture classification. © 2005 Taylor & Francis. |
Persistent Identifier | http://hdl.handle.net/10722/296591 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.776 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Li, Y. | - |
dc.contributor.author | Gong, P. | - |
dc.date.accessioned | 2021-02-25T15:16:13Z | - |
dc.date.available | 2021-02-25T15:16:13Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | International Journal of Remote Sensing, 2005, v. 26, n. 22, p. 5149-5159 | - |
dc.identifier.issn | 0143-1161 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296591 | - |
dc.description.abstract | Texture analysis of remote sensing images based on classification of area units represented in image segments is usually more accurate than operating on an individual pixel basis. In this paper we suggest a two-step procedure to segment texture patterns in remotely sensed data. An image is first classified based on texture analysis using a multi-parameter and multi-scale technique. The intermediate results are then treated as initial segments for subsequent segmentation based on the Gaussian Markov random field (GMRF) model. The segmentation procedure seeks to merge pairs of segments with the minimum variance difference. Experiments using real data prove that the two-step procedure improves both computational efficiency and accuracy of texture classification. © 2005 Taylor & Francis. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Remote Sensing | - |
dc.title | An efficient texture image segmentation algorithm based on the GMRF model for classification of remotely sensed imagery | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/01431160500176838 | - |
dc.identifier.scopus | eid_2-s2.0-33745091249 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 22 | - |
dc.identifier.spage | 5149 | - |
dc.identifier.epage | 5159 | - |
dc.identifier.eissn | 1366-5901 | - |
dc.identifier.isi | WOS:000234407300016 | - |
dc.identifier.issnl | 0143-1161 | - |