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Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1552 - 1567 Titre : Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Anudeep Sure, Auteur ; Onkar Dikshit, Auteur Année de publication : 2019 Article en page(s) : pp 1552 - 1567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Inde [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies. Numéro de notice : A2019-527 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1497096 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1497096 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94101 [article]

Evaluation of global geopotential models: a case study for India / Ropesh Goyal in Survey review, vol 51 n° 368 (September 2019)  

Public [article]  inSurvey review > vol 51 n° 368 (September 2019) . - pp 402 - 412 Titre : Evaluation of global geopotential models: a case study for India Type de document : Article/Communication Auteurs : Ropesh Goyal, Auteur ; Onkar Dikshit, Auteur ; Nagarajan Balasubramania, Auteur Année de publication : 2019 Article en page(s) : pp 402 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] Inde [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] modèle de géopotentiel [Termes descripteurs IGN] modèle de géopotentiel local [Termes descripteurs IGN] réseau altimétrique national Résumé : (Auteur) This paper aims to identify most suitable global geopotential model (GGM) for India, by comparing 15 GGMs developed through 1996 to 2017. The GGM derived geoid undulation values are compared with the geometrical undulation values obtained from GNSS/levelling data on Indian vertical datum. A correction term is added to the computed GGM derived geoid undulation value after fitting three-, four-, five- and seven-parameter models to account for bias and tilt between local geometric Indian vertical datum and global gravimetric vertical datum. The results indicate that EGM2008 model is the best GGM available for India with root-mean-square error (RMSE) of 0.28 m, without model fitting. However, after considering the systematic errors in the two datums with seven-parameter model, GECO GGM has shown significantly better results with RMSE of 0.19 m for India. Numéro de notice : A2019-366 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1468537 date de publication en ligne : 11/05/2018 En ligne : https://doi.org/10.1080/00396265.2018.1468537 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93472 [article]

Identifying geospatial services across heterogeneous taxonomies / Anand Mehta in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)  

Public [article]  inGeocarto international > Vol 31 n° 9 - 10 (October - November 2016) . - pp 1058 - 1077 Titre : Identifying geospatial services across heterogeneous taxonomies Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Akash Ashapure, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 1058 - 1077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] identification automatique [Termes descripteurs IGN] informatique [Termes descripteurs IGN] service fondé sur la position [Termes descripteurs IGN] taxinomie Résumé : (auteur) Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach. Numéro de notice : A2016-674 Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1110208 date de publication en ligne : 02/12/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1110208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81924 [article]

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Comparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 296 - 307 Titre : Comparative study on projected clustering methods for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 296 - 307 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] Orclus Résumé : (Auteur) In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction. Numéro de notice : A2016-152 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047416 date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80385 [article]

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