Unsupervised change detection on SAR images using fuzzy hidden Markov chains
| Autor: | Cyril Carincotte, Salah Bourennane, Stéphane Derrode |
|---|---|
| Přispěvatelé: | Institut FRESNEL (FRESNEL), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Bourennane, Salah |
| Jazyk: | angličtina |
| Rok vydání: | 2006 |
| Předmět: |
Synthetic aperture radar
Computer science Fuzzy set ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies 02 engineering and technology Lebesgue integration Fuzzy logic symbols.namesake Radar imaging 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Hidden Markov model 021101 geological & geomatics engineering Contextual image classification business.industry Detector Pattern recognition Image segmentation [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] symbols General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR] business Change detection |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2006, 44, pp.432-441 IEEE Transactions on Geoscience and Remote Sensing, 2006, 44, pp.432-441 |
| ISSN: | 0196-2892 |
| Popis: | This work deals with unsupervised change detection in temporal sets of synthetic aperture radar (SAR) images. We focus on one of the most widely used change detector in the SAR context, the so-called log-ratio. In order to deal with the classification issue, we propose to use a new fuzzy version of hidden Markov chains (HMCs), and thus to address fuzzy change detection with a statistical approach. The main characteristic of the proposed model is to simultaneously use Dirac and Lebesgue measures at the class chain level. This allows the coexistence of hard pixels (obtained with the classical HMC segmentation) and fuzzy pixels (obtained with the fuzzy measure) in the same image. The quality assessment of the proposed method is achieved with several bidate sets of simulated images, and comparisons with classical HMC are also provided. Experimental results on real European Remote Sensing 2 Precision Image (ERS-2 PRI) images confirm the effectiveness of the proposed approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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