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Adaptive registration of remote sensing images using supervised learning / L. Eikvil in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 11 (November 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 11 (November 2009) . - pp 1297 - 1306 Titre : Adaptive registration of remote sensing images using supervised learning Type de document : Article/Communication Auteurs : L. Eikvil, Auteur ; M. Holden, Auteur ; R.B. Huseby, Auteur Année de publication : 2009 Article en page(s) : pp 1297 - 1306 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement d'images [Termes IGN] apprentissage dirigé [Termes IGN] image satellite [Termes IGN] série temporelle [Termes IGN] superposition d'images Résumé : (Auteur) This paper describes a system for co-registration of time series satellite images which uses a learning-based strategy. During a training phase, the system learns to recognize regions in an image suited for registration. It also learns the relationship between image characteristics and registration performance for a set of different registration algorithms. This enables intelligent selection of an appropriate registration algorithm for each region in the image, while regions unsuited for registration can be discarded. The approach is intended for co-registration of sequences of images acquired from identical or similar earth observation sensors. It has been tested for such sequences from different types of sensors, both optical and radar, with varying resolution. For images with moderate differences in content, the registration accuracy is, in general, good with an RMS error of one pixel or less. Copyright ASPRS Numéro de notice : A2009-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.11.1297 En ligne : https://doi.org/10.14358/PERS.75.11.1297 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30073 [article]

A matching algorithm for detecting land use changes using case-based reasoning / X. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 11 (November 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 11 (November 2009) . - pp 1319 - 1332 Titre : A matching algorithm for detecting land use changes using case-based reasoning Type de document : Article/Communication Auteurs : X. Li, Auteur ; A.G. Yeh, Auteur ; J. Qian, Auteur ; Z. Qi, Auteur Année de publication : 2009 Article en page(s) : pp 1319 - 1332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] algorithme génétique [Termes IGN] analyse diachronique [Termes IGN] analyse spatiale [Termes IGN] classification barycentrique [Termes IGN] classification orientée objet [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] segmentation d'image [Termes IGN] utilisation du sol Résumé : (Auteur) The paper deals with change detection using time series SAR images. SAR provides a unique opportunity for detecting land-use changes within short intervals (e.g., monthly) in tropical and sub-tropical regions with cloud cover. Traditional change detection methods mainly rely on per-pixel spectral information but ignore per-object structural information. In this study, a new method is presented that integrates object-oriented analysis with case-based reasoning (CBR) for change detection. Object-oriented analysis is carried out to retrieve a variety of features, such as tone, shape, texture, area, and context. An incremental segmentation technique is proposed for deriving change objects from multi-temporal Radarsat images. Feature selection based on genetic algorithms is carried out to determine the optimal set of features for change detection. A CBR matching algorithm is developed to identify the temporal positions and the kind of changes. It is based on the weighted k-Nearest Neighbor classification using an accumulative similarity measure. The comparison of the four combinations of change detection methods, object-based or pixel-based plus case-based or rule-based, is carried out to validate the performance of this proposed method. The analysis shows that this integrated approach has provided an efficient way of detecting land-use changes at monthly intervals by using multi-temporal SAR images. Copyright ASPRS Numéro de notice : A2009-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.11.1319 En ligne : https://doi.org/10.14358/PERS.75.11.1319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30074 [article]