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Toward a generalizable image representation for large-scale change detection : application to generic damage analysis / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3378 - 3387 Titre : Toward a generalizable image representation for large-scale change detection : application to generic damage analysis Type de document : Article/Communication Auteurs : Lionel Gueguen, Auteur ; Raffay Hamid, Auteur Année de publication : 2016 Article en page(s) : pp 3378 - 3387 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse combinatoire (maths) [Termes IGN] analyse spatiale [Termes IGN] codage [Termes IGN] détection automatique [Termes IGN] géovisualisation [Termes IGN] image à haute résolution [Termes IGN] image multicapteur Résumé : (Auteur) Each year, multiple catastrophic events impact vulnerable populations around the planet. Assessing the damage caused by these events in a timely and accurate manner is crucial for efficient execution of relief efforts to help the victims of these calamities. Given the low accessibility of the damaged areas, high-resolution optical satellite imagery has emerged as a valuable source of information to quickly asses the extent of damage by manually analyzing the pre- and postevent imagery of the region. To make this analysis more efficient, multiple learning techniques using a variety of image representations have been proposed. However, most of these representations are prone to variabilities in capture angle, sun location, and seasonal variations. To evaluate these representations in the context of damage detection, we present a benchmark of 86 pre- and postevent image pairs with respective reference data derived from United Nation Operational Satellite Applications Programme (UNOSAT) assessment maps, spanning a total area of 4665 km2 from 11 different locations around the world. The technical contribution of our work is a novel image representation based on shape distributions of image patches encoded with locality-constrained linear coding. We empirically demonstrate that our proposed representation provides an improvement of at least 5%, in equal error rate, over alternate approaches. Finally, we present a thorough robustness analysis of the considered representational schemes, with respect to capture-angle variabilities and multiple sensor combinations. Numéro de notice : A2016-852 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2516402 En ligne : https://doi.org/10.1109/TGRS.2016.2516402 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82986 [article]

Classifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 4 (April 2015) . - pp 1803 - 1818 Titre : Classifying compound structures in satellite images : A compressed representation for fast queries Type de document : Article/Communication Auteurs : Lionel Gueguen, Auteur Année de publication : 2015 Article en page(s) : pp 1803 - 1818 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification orientée objet [Termes IGN] image multibande [Termes IGN] image optique [Termes IGN] occupation du sol [Termes IGN] segmentation d'image Résumé : (Auteur) With the increased spatial resolution of current sensor constellations, more details are captured about our changing planet, enabling the recognition of a greater range of land use/land cover classes. While pixeland object-based classification approaches are widely used for extracting information from imagery, recent studies have shown the importance of spatial contexts for discriminating more specific and challenging classes. This paper proposes a new compact representation for the fast query/classification of compound structures from very high resolution optical remote sensing imagery. This bag-of-features representation relies on the multiscale segmentation of the input image and the quantization of image structures pooled into visual word distributions for the characterization of compound structures. A compressed form of the visual word distributions is described, allowing adaptive and fast queries/classification of image patterns. The proposed representation and the query methodology are evaluated for the classification of the UC Merced 21-class data set, for the detection of informal settlements and for the discrimination of challenging agricultural classes. The results show that the proposed representation competes with state-of-the-art techniques. In addition, the complexity analysis demonstrates that the representation requires about 5% of the image storage space while allowing us to perform queries at a speed down to 1 s/ 1000 km2/CPU for 2-m multispectral data. Numéro de notice : A2015-175 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2348864 Date de publication en ligne : 04/09/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2348864 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75894 [article]

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