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An iterative interpolation deconvolution algorithm for superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7210 - 7222 Titre : An iterative interpolation deconvolution algorithm for superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yong Ge, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7210 - 7222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification du maximum a posteriori [Termes IGN] déconvolution [Termes IGN] image à ultra haute résolution [Termes IGN] itération Résumé : (Auteur) Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms. Numéro de notice : A2016-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598534 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83342 [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]

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]