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ARRSI: Automatic Registration of Remote-Sensing Images / A. Wong in IEEE Transactions on geoscience and remote sensing, vol 45 n° 5 Tome 2 (May 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 5 Tome 2 (May 2007) . - pp 1483 - 1493 Titre : ARRSI: Automatic Registration of Remote-Sensing Images Type de document : Article/Communication Auteurs : A. Wong, Auteur ; D.A. Clausi, Auteur Année de publication : 2007 Article en page(s) : pp 1483 - 1493 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] congruence [Termes IGN] distorsion d'image [Termes IGN] image multicapteur [Termes IGN] image multitemporelle [Termes IGN] point de canevas [Termes IGN] superposition d'images Résumé : (Auteur) This paper presents the Automatic Registration of Remote-Sensing Images (ARRSI); an automatic registration system built to register satellite and aerial remotely sensed images. The system is designed specifically to address the problems associated with the registration of remotely sensed images obtained at different times and/or from different sensors. The ARRSI system is capable of handling remotely sensed images geometrically distorted by various transformations such as translation, rotation, and shear. Global and local contrast issues associated with remotely sensed images are addressed in ARRSI using control-point detection and matching processes based on a phase-congruency model. Intensity-difference issues associated with multimodal registration of remotely sensed images are addressed in ARRSI through the use of features that are invariant to intensity mappings during the control-point matching process. An adaptive control-point matching scheme is employed in ARRSI to reduce the performance issues associated with the registration of large remotely sensed images. Finally, a variation on the Random Sample and Consensus algorithm called Maximum Distance Sample Consensus is introduced in ARRSI to improve the accuracy of the transformation model between two remotely sensed images while minimizing computational overhead. The ARRSI system has been tested using various satellite and aerial remotely sensed images and evaluated based on its accuracy and computational performance. The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested. Copyright IEEE Numéro de notice : A2007-295 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.892601 En ligne : https://doi.org/10.1109/TGRS.2007.892601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28658 [article]

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Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery / D.A. Clausi in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 1 (January 2004) . - pp 215 - 228 Titre : Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery Type de document : Article/Communication Auteurs : D.A. Clausi, Auteur ; B. Yue, Auteur Année de publication : 2004 Article en page(s) : pp 215 - 228 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] champ aléatoire de Markov [Termes IGN] glace [Termes IGN] image Radarsat [Termes IGN] niveau de gris (image) [Termes IGN] segmentation d'image [Termes IGN] télédétection spatiale [Termes IGN] texture d'image Résumé : (Auteur) This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive in texture boundary confusion than MRFs given their respective segmentation procedures. Numéro de notice : A2004-045 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.817218 En ligne : https://doi.org/10.1109/TGRS.2003.817218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26573 [article]

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