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A new statistical similarity measure for change detection in multi-temporal SAR images and its extension to multi-scale change analysis / Jordi Inglada 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 1432 - 1445 Titre : A new statistical similarity measure for change detection in multi-temporal SAR images and its extension to multi-scale change analysis Type de document : Article/Communication Auteurs : Jordi Inglada, Auteur ; Grégoire Mercier, Auteur Année de publication : 2007 Article en page(s) : pp 1432 - 1445 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse multiéchelle [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image TerraSAR-X [Termes IGN] pixel Résumé : (Auteur) In this paper, we present a new similarity measure for automatic change detection in multitemporal synthetic aperture radar images. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are estimated by using a cumulant-based series expansion, which approximates probability density functions in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler divergence. An analytical expression for this detector is given, allowing a simple computation which depends on the four first statistical moments of the pixels inside the analysis window only. The proposed change indicator is compared to the classical mean ratio detector and also to other model-based approaches. Tests on the simulated and real data show that our detector outperforms all the others. The fast computation of the proposed detector allows a multiscale approach in the change detection for operational use. The so-called multiscale change profile (MCP) is introduced to yield change information on a wide range of scales and to better characterize the appropriate scale. Two simple yet useful examples of applications show that the MCP allows the design of change indicators, which provide better results than a monoscale analysis. Copyright IEEE Numéro de notice : A2007-294 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.893568 En ligne : https://doi.org/10.1109/TGRS.2007.893568 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28657 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07051B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07051B	RAB	Revue	Centre de documentation	En réserve L003	Disponible