Centre de documentation
scientifique

Usefulness of synthetic aperture radar (SAR) interferometry for digital elevation model (DEM) generation and estimation of land surface displacement in Jharia coal field area / Atanu Bhattacharya in Geocarto international, vol 27 n° 1 (February 2012)  

Public [article]  inGeocarto international > vol 27 n° 1 (February 2012) . - pp 57 - 77 Titre : Usefulness of synthetic aperture radar (SAR) interferometry for digital elevation model (DEM) generation and estimation of land surface displacement in Jharia coal field area Type de document : Article/Communication Auteurs : Atanu Bhattacharya, Auteur ; M. Arora, Auteur ; M. Sharma, Auteur Année de publication : 2012 Article en page(s) : pp 57 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] appariement de formes [Termes IGN] effondrement de terrain [Termes IGN] image radar moirée [Termes IGN] Inde [Termes IGN] interferométrie différentielle [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] mine de charbon [Termes IGN] modèle numérique de surface [Termes IGN] mouvement de terrain Résumé : (Auteur) Land surface displacement is a phenomenon of ground movement, which may occur due to various reasons including unplanned mining. The quantification of land surface displacement through conventional field surveys is based on sparingly distributed point data, which may be insufficient for many applications. A detailed spatial and temporal monitoring of land surface displacements through remote sensing-based synthetic aperture radar (SAR) interferometry may be valuable. Over the last two decades, differential SAR interferometry (DInSAR) has been effectively used globally for the estimation of spatial land surface displacements caused due to natural and man-made hazards. However, it has not gained momentum in India, where occurrences of natural and man-made hazards are a common phenomenon. In this article, preliminary results from DInSAR to measure land surface displacement in Jharia coal fields have been presented. DInSAR results effectively identified the land surface displacement due to several mining activities in the region during a one-month period. Numéro de notice : A2012-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.614358 Date de publication en ligne : 26/10/2011 En ligne : https://doi.org/10.1080/10106049.2011.614358 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31551 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2012011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers / P. Watanachaturaporn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 2 (February 2008) . - pp 239 - 246 Titre : Multisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers Type de document : Article/Communication Auteurs : P. Watanachaturaporn, Auteur ; M. Arora, Auteur ; K. Varshney, Auteur Année de publication : 2008 Article en page(s) : pp 239 - 246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification par arbre de décision [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données multisources [Termes IGN] extraction automatique [Termes IGN] Himalaya [Termes IGN] image IRS-LISS [Termes IGN] Kappa de Cohen [Termes IGN] modèle numérique de surface [Termes IGN] occupation du sol Résumé : (Auteur) Remote sensing image classification has proven to be attractive for extracting useful thematic information such as landcover. However, often for a given application, spectral information acquired by a remote sensing sensor may not be sufficient to derive accurate information. Incorporation of data from other sources such as a digital elevation model (DEM), and geophysical and geological data may assist in achieving more accurate land-cover classification from remote sensing images. Recently, support vector machines (SVM) have been proposed as an alternative for classification of remote sensing data, and the results are promising. In this paper, we employ the SVM algorithm to perform multisource classification. An IRS–1C LISS III image along with normalized differenced vegetation index (NDVI) image and DEM are used to produce a land-cover classification for a region in the Himalayas. The accuracy of SVM-based multisource classification is compared with several other nonparametric algorithms namely a decision tree classifier, and back propagation and radial basis function neural network classifiers. The well-known kappa coefficient of agreement is used to assess classification accuracy. The differences in the kappa coefficient of classifiers have been statistically evaluated using a pairwise Z-test. The results show a significant increase in the accuracy of the SVM based classifier on incorporation of ancillary data over classification performed solely on the basis of spectral data from remote sensing sensors. Copyright ASPRS Numéro de notice : A2008-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.2.239 En ligne : https://doi.org/10.14358/PERS.74.2.239 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29043 [article]