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Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)  

Public [article]  inGeocarto international > vol 32 n° 6 (June 2017) . - pp 640 - 654 Titre : Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis Type de document : Article/Communication Auteurs : Arati Paul, Auteur ; V.M. Chowdary, Auteur ; Y.K. Srivastava, Auteur ; Debsunder Dutta, Auteur ; J.R. Sharma, Auteur Année de publication : 2017 Article en page(s) : pp 640 - 654 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Calcutta [Termes IGN] densité spectrale de puissance [Termes IGN] détection de changement [Termes IGN] détection de contours [Termes IGN] Google Earth [Termes IGN] image Cartosat-1 [Termes IGN] image IRS-LISS [Termes IGN] occupation du sol [Termes IGN] seuillage d'image Résumé : (Auteur) Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively. Numéro de notice : A2017-275 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1167966 Date de publication en ligne : 01/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1167966 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85304 [article]

Characterizing vegetation canopy structure using airborne remote sensing data / Debsunder Dutta in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 2 (February 2017) . - pp 1160 - 1178 Titre : Characterizing vegetation canopy structure using airborne remote sensing data Type de document : Article/Communication Auteurs : Debsunder Dutta, Auteur ; Kunxuan Wang, Auteur ; Esther Lee, Auteur Année de publication : 2017 Article en page(s) : pp 1160 - 1178 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] canopée [Termes IGN] densité de la végétation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] feuille (végétation) [Termes IGN] forêt ripicole [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier [Termes IGN] voxel Résumé : (Auteur) Vegetation canopy structure plays an important role in the partitioning of incident solar radiation, photosynthesis, transpiration, and other scalar fluxes. The vertical foliage distribution of the plant canopy is represented by leaf area density (LAD), which is defined as the one-sided leaf area per unit volume. Airborne light detection and ranging (LiDAR) offers the possibility to characterize the 3-D variation of LAD over space, which still remains a challenge to estimate. Moreover, the low density of point cloud data generally offered by airborne LiDAR may be insufficient for accurate LAD estimation in dense overlapping forest canopies. We develop a method for the estimation of the LAD profile using a combination of airborne LiDAR and hyperspectral data using a feature-based data fusion approach. After identifying vegetation species using hyperspectral data, point cloud LiDAR data is used in a “tree-shaped” voxel approach to characterize the LAD of trees in a riparian forest setting. We also propose a set of relationships on simple geometry of overlap for the construction of tree shaped voxels. In a forest setting with overlapping canopies, the results indicate that the tree-shaped voxels are better able to attribute the LAD to the upper and middle parts of the overall canopy as well as individual tall and short trees compared with traditional cylindrical voxels. Numéro de notice : A2017-147 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2620478 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2620478 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84635 [article]