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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]

A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li 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 3822 - 3841 Titre : A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions Type de document : Article/Communication Auteurs : Xiaodong Li, Auteur ; Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yun Du, Auteur Année de publication : 2016 Article en page(s) : pp 3822 - 3841 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse des mélanges spectraux [Termes IGN] changement d'occupation du sol [Termes IGN] classification pixellaire [Termes IGN] détection de changement [Termes IGN] image à moyenne résolution [Termes IGN] image à très haute résolution [Termes IGN] image Landsat-OLI [Termes IGN] image multibande [Termes IGN] image Terra-MODIS [Termes IGN] itération Résumé : (auteur) The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a trade-off between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse spatial resolution images have high temporal repetition rates. A novel super-resolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and super-resolution land-cover mapping. Both the land-cover change/no-change map and from–to change map at fine spatial resolution can be generated by SRCD. In this study, SRCD was applied to synthetic multispectral image, Moderate-Resolution Imaging Spectroradiometer (MODIS) multispectral image and Landsat-8 Operational Land Imager (OLI) multispectral image. The land-cover from–to change maps are found to have the highest overall accuracy (higher than 85%) in all the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected. Numéro de notice : A2016--122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2528583 En ligne : https://doi.org/10.1109/TGRS.2016.2528583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84900 [article]

Sub-pixel-scale land cover map updating by integrating change detection and sub-pixel mapping / Xiaodong Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 1 (January 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 1 (January 2015) . - pp 59 - 67 Titre : Sub-pixel-scale land cover map updating by integrating change detection and sub-pixel mapping Type de document : Article/Communication Auteurs : Xiaodong Li, Auteur ; Yun Du, Auteur ; Feng Ling, Auteur Année de publication : 2015 Article en page(s) : pp 59 - 67 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse infrapixellaire [Termes IGN] carte d'occupation du sol [Termes IGN] détection de changement [Termes IGN] image à basse résolution [Termes IGN] implémentation (informatique) [Termes IGN] mise à jour automatique [Termes IGN] précision infrapixellaire Résumé : (auteur) Course-resolution remotely sensed images are high in temporal repetition rates, but their low spatial resolution limits their application in updating land cover maps. Our proposed land cover updating method involves the use of coarse-resolution images to update fine-resolution land cover maps. The method comprises change detection and sub-pixel mapping methods. The current coarse-resolution image is unmixed, and the previous fine-resolution map is spatially degraded to produce current and previous class fraction images. A change detection method is applied to these fraction images to create a fine-resolution binary change/non-change map. Finally, a sub-pixel mapping method is applied to update the fine-resolution pixel labels that are changed in the change/ non-change map. The proposed method is compared with a pixel-based classification method and two sub-pixel mapping methods. The proposed method maintains most of the spatial patterns of land cover classes that are unchanged in the previous and current images, whereas other methods cannot. Numéro de notice : A2015-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.1.59 En ligne : http://www.ingentaconnect.com/content/asprs/pers/2015/00000081/00000001/art00004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75151 [article]