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Learning-based spatial-temporal superresolution mapping of forest cover with MODIS images / Yihang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 600 - 614 Titre : Learning-based spatial-temporal superresolution mapping of forest cover with MODIS images Type de document : Article/Communication Auteurs : Yihang Zhang, Auteur ; Peter M. Atkinson, Auteur ; Xiaodong Li, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 600 - 614 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algorithme d'apprentissage [Termes IGN] carte forestière [Termes IGN] couvert forestier [Termes IGN] déboisement [Termes IGN] données spatiotemporelles [Termes IGN] image à très haute résolution [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] surveillance forestière Résumé : (Auteur) Forest mapping from satellite sensor imagery provides important information for the timely monitoring of forest growth and deforestation, bioenergy potential assessment, and modeling of carbon flux, among others. Due to the daily global revisit rate and wide swath width, MODerate-resolution Imaging Spectroradiometer (MODIS) images are used commonly for satellite-derived forest mapping at both regional and global scales. However, the spatial resolution of MODIS images is too coarse to observe fine spatial variation in forest cover. The last few decades have seen the production of several fine-spatial-resolution satellite-derived global forest cover maps, such as Hansen's global tree canopy cover map of 2000, which includes abundant spectral, temporal, and spatial prior information about forest cover at a fine spatial resolution. In this paper, a novel learning-based spatial-temporal superresolution mapping approach is proposed to integrate both current MODIS images and prior maps of Hansen's tree canopy cover, to map present forest cover with a fine spatial resolution. The novel approach is composed of three main stages: 1) automatic generation of 240-m forest proportion images from both 240- and 480-m MODIS images using a nonlinear learning-based spectral unmixing method; 2) downscaling the 240-m forest proportion images to 30 m to predict the class possibilities at the subpixel scale using a temporal-example learning-based downscaling method; and 3) final production of the fine-spatial-resolution forest map by solving a regularization-based optimization problem. The novel approach produced more accurate fine-spatial-resolution forest cover maps in terms of both visual and quantitative evaluation than traditional pixel-based classification and the latest subpixel based superresolution mapping methods. The results show the great efficiency and potential of the novel approach for producing fine-spatial-resolution forest maps from MODIS images. Numéro de notice : A2017-023 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2613140 En ligne : https://doi.org/10.1109/TGRS.2016.2613140 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83955 [article]

Learning-based superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 3794 - 3810 Titre : Learning-based superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Yihang Zhang, Auteur ; Giles M. Foody, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3794 - 3810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algorithme d'apprentissage [Termes IGN] base de données localisées [Termes IGN] géovisualisation [Termes IGN] image à très haute résolution [Termes IGN] occupation du sol [Termes IGN] pouvoir de résolution géométrique [Termes IGN] représentation des données Résumé : (Auteur) Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarse-spatial-resolution fraction images estimated by soft classification. The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose prior model is learned from other available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent fine-spatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRM methods using land cover map subsets generated from the USA's National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment. Numéro de notice : A2016-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2527841 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2527841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83029 [article]