A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model
In this letter, a robust and fast unsupervised change-detection framework is proposed for synthetic aperture radar (SAR) images. It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2013-01, Vol.10 (1), p.14-18 |
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| creator | Zhang, Xiaohua Chen, Jiawei Meng, Hongyun |
| description | In this letter, a robust and fast unsupervised change-detection framework is proposed for synthetic aperture radar (SAR) images. It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed regions and enhance the change information synchronously. Then, each class of the difference image is modeled by generalized Gaussian distribution (GGD), and its parameters are learned by the expectation-maximization algorithm. Moreover, the graph-cut algorithm is employed on the difference image to extract the spatial prior information, based on which the parameters of GGD are initialized well via the fuzzy c -means algorithm. Finally, the Bayesian inference for maximum a posteriori performs the final detection. Experimental results on simulated and real SAR data sets confirm the robustness and accuracy of the proposed algorithm in which graph-cut and GGD make great contribution on improving the accuracy of detection and speed of algorithm. |
| doi_str_mv | 10.1109/LGRS.2012.2189867 |
| format | Article |
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Experimental results on simulated and real SAR data sets confirm the robustness and accuracy of the proposed algorithm in which graph-cut and GGD make great contribution on improving the accuracy of detection and speed of algorithm.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2012.2189867</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Algorithms ; Change detection ; Change detection algorithms ; Convergence ; Estimation ; expectation maximization ; Fuzzy ; generalized Gaussian model ; graph cut ; Inference ; Mathematical models ; Noise measurement ; Robustness ; Speckle ; Studies ; Synthetic aperture radar</subject><ispartof>IEEE geoscience and remote sensing letters, 2013-01, Vol.10 (1), p.14-18</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c326t-60350a8263ec4b2319199ee583c496d04c3b1c785f726b919fb792bb618fb4723</citedby><cites>FETCH-LOGICAL-c326t-60350a8263ec4b2319199ee583c496d04c3b1c785f726b919fb792bb618fb4723</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6204324$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6204324$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Xiaohua</creatorcontrib><creatorcontrib>Chen, Jiawei</creatorcontrib><creatorcontrib>Meng, Hongyun</creatorcontrib><title>A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>In this letter, a robust and fast unsupervised change-detection framework is proposed for synthetic aperture radar (SAR) images. It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed regions and enhance the change information synchronously. Then, each class of the difference image is modeled by generalized Gaussian distribution (GGD), and its parameters are learned by the expectation-maximization algorithm. Moreover, the graph-cut algorithm is employed on the difference image to extract the spatial prior information, based on which the parameters of GGD are initialized well via the fuzzy c -means algorithm. Finally, the Bayesian inference for maximum a posteriori performs the final detection. Experimental results on simulated and real SAR data sets confirm the robustness and accuracy of the proposed algorithm in which graph-cut and GGD make great contribution on improving the accuracy of detection and speed of algorithm.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Change detection</subject><subject>Change detection algorithms</subject><subject>Convergence</subject><subject>Estimation</subject><subject>expectation maximization</subject><subject>Fuzzy</subject><subject>generalized Gaussian model</subject><subject>graph cut</subject><subject>Inference</subject><subject>Mathematical models</subject><subject>Noise measurement</subject><subject>Robustness</subject><subject>Speckle</subject><subject>Studies</subject><subject>Synthetic aperture radar</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkEFLw0AUhIMoWKs_QLwsePGSum93s9k91qixUBXaCt7CJnmxkTSpu4mgv97EFg-e3sB8MzzG886BTgCovp7Hi-WEUWATBkorGR54IwgC5dMghMNBi8APtHo99k6ce6eUCaXCkbeakqfmEyuynC7IbGPekERrU_fnFlvM2rKpyY1xmJNexNZs137UtcTUOYmxRmuq8rs3Y9M5V5qaPDY5VqfeUWEqh2f7O_Ze7u9W0YM_f45n0XTuZ5zJ1peUB9QoJjlmImUcNGiNGCieCS1zKjKeQhaqoAiZTHuzSEPN0lSCKlIRMj72rna9W9t8dOjaZFO6DKvK1Nh0LgHgUjAAJXr08h_63nS27r9LgHJNQYa_hbCjMts4Z7FItrbcGPvVQ8mwczLsnAw7J_ud-8zFLlMi4h8vGRWcCf4DuBN16w</recordid><startdate>201301</startdate><enddate>201301</enddate><creator>Zhang, Xiaohua</creator><creator>Chen, Jiawei</creator><creator>Meng, Hongyun</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed regions and enhance the change information synchronously. Then, each class of the difference image is modeled by generalized Gaussian distribution (GGD), and its parameters are learned by the expectation-maximization algorithm. Moreover, the graph-cut algorithm is employed on the difference image to extract the spatial prior information, based on which the parameters of GGD are initialized well via the fuzzy c -means algorithm. Finally, the Bayesian inference for maximum a posteriori performs the final detection. 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| identifier | ISSN: 1545-598X |
| ispartof | IEEE geoscience and remote sensing letters, 2013-01, Vol.10 (1), p.14-18 |
| issn | 1545-598X 1558-0571 |
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| subjects | Accuracy Algorithms Change detection Change detection algorithms Convergence Estimation expectation maximization Fuzzy generalized Gaussian model graph cut Inference Mathematical models Noise measurement Robustness Speckle Studies Synthetic aperture radar |
| title | A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model |
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