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Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 801 - 817 Titre : Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery Type de document : Article/Communication Auteurs : Zhenhui Sun, Auteur ; Qingyan Meng, Auteur Année de publication : 2020 Article en page(s) : pp 801 - 817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] optimisation par essaim de particules [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) The WorldView-2 high spatial resolution satellite with eight multispectral imaging bands is ideally suited for extracting built-up areas (BUs) from remote sensing images. In this study, an object-based automatic multi-index BUs extraction method was developed. First, several indices, including BUs extraction index (NBEIr-c), vegetation extraction index(NDVInir2-r) and water extraction index (NDWI b-nir1), were developed to obtain the BUs, vegetation and water maps, and then the fractional-order Darwinian particle swarm optimization (FODPSO) algorithm was employed to automatically segment the multi-index images and obtained BUs, water, vegetation and bare soil (BS) information. Finally, the extracted BUs results were optimized via an object-based analysis method and the results were compared with those of two other relevant indices, which confirmed the proposed method had a higher accuracy and exhibited higher performance when separating the BS from the BUs. Numéro de notice : A2020-273 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1544290 date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1544290 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95058 [article]

Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India / Subrata Mondal in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 818 - 856 Titre : Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India Type de document : Article/Communication Auteurs : Subrata Mondal, Auteur ; Sujit Mandal, Auteur Année de publication : 2020 Article en page(s) : pp 818 - 856 Note générale : bibbliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] action anthropique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] lithologie [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] théorie de Dempster-Shafer [Termes descripteurs IGN] vulnérabilité Résumé : (auteur) In the present study, data-driven evidential belief function model (belief function) was employed to generate landslides susceptibility index map of Darjeeling Himalaya considering 15 landslide causative factors, which grouped into six categories, i.e. geomorphological factors (elevation, aspect, slope, curvature), lithological factors (geology, soil, lineament density, distance to lineament), hydrologic factors (drainage density, distance to drainage, stream power index, topographic wetted index), triggering factor (rainfall), protective factor (normalized differential vegetation index) and anthropogenic factor (land use and land cover). Total 2079 landslide locations were mapped and randomly divided it into training datasets (70% landslide locations) and validation datasets (30% landslide locations). The resultant susceptibility map was divided into five different susceptibility zones i.e. very low, low, moderate, high and very high which covered 5.60%, 25.65%, 34.47%, 24.67% and 9.61% area respectively of the Darjeeling Himalaya. Receiver operating characteristics curve suggested that 80.20% prediction accuracy of the prepared map whereas frequency ratio plot indicated towards the ideal landslides susceptibility index map. Numéro de notice : A2020-274 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2018.1544288 date de publication en ligne : 13/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1544288 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95059 [article]

Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 905 - 915 Titre : Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data Type de document : Article/Communication Auteurs : Thota Sivasankar, Auteur ; Dheeraj Kumar, Auteur ; Hari Shanker Srivastava, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 905 - 915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande C [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Risat-1 [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] régression non linéaire [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Leaf Area Index (LAI) is a key parameter to characterize the canopy–atmosphere interface, where most of the energy fluxes exchange. Space-borne satellite images have shown their relevance for various applications including LAI retrieval over large areas. Although optical data have been used for this purpose in previous studies, the constraints to acquire optical data during extreme weather conditions due to the presence of clouds, haze, smoke etc. hinders its use for uninterrupted monitoring. This study aims to analyze the relationships of C-band RISAT-1 hybrid polarized SAR data (σ˚RH and σ˚RV) with wheat LAI. The results have shown the correlation coefficient (|r|) of 0.57 and 0.73 for RH and RV backscatter, respectively, using non-linear regression approach. It is also observed that the accuracy of LAI retrieval has been significantly improved with |r| and RMSE of 0.81 and 0.54 (m2/m2), respectively, by considering both RH and RV backscatter as inputs for support vector machine-based model. Numéro de notice : A2020-341 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2019.1566404 date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566404 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95219 [article]