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Contextual reconstruction of cloud-contaminated multitemporal multispectral image / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 44 n° 2 (February 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 2 (February 2006) . - pp 442 - 455 Titre : Contextual reconstruction of cloud-contaminated multitemporal multispectral image Type de document : Article/Communication Auteurs : F. Melgani, Auteur Année de publication : 2006 Article en page(s) : pp 442 - 455 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification non dirigée [Termes IGN] filtrage du rayonnement [Termes IGN] image Landsat-ETM+ [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] nébulosité [Termes IGN] prévision linéaire [Termes IGN] prévision non-linéaire [Termes IGN] prise en compte du contexte [Termes IGN] reconstruction d'image Résumé : (Auteur) The frequent presence of clouds in passive remotely sensed imagery severely limits its regular exploitation in various application fields. Thus, the removal of cloud cover from this imagery represents an important preprocessing task consisting in the reconstruction of cloud-contaminated data. The intent of this study is to propose two novel general methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images. Given a cloud-contaminated image of the sequence, each area of missing measurements is reconstructed through an unsupervised contextual prediction process that reproduces the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. In the first method, the contextual prediction process is implemented by means of an ensemble of linear predictors, each trained over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected subset. In order to obtain such regions, each temporal image is locally classified by an unsupervised classifier based on the expectation-maximization (EM) algorithm. In the second method, the local spectro-temporal relationships are reproduced by a single nonlinear predictor based on the support vector machines (SVM) approach. To illustrate the performance of the two proposed methods, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor over a total period of four months is reported and discussed. It includes a detailed simulation study that aims at assessing with different reconstruction quality criteria the accuracy of the methods in different qualitative and quantitative cloud contamination conditions. Compared with two techniques based on compositing algorithms for cloud removal, the proposed methods show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance. Numéro de notice : A2006-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.861929 En ligne : https://doi.org/10.1109/TGRS.2005.861929 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27853 [article]

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