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Auteur Chintan A. Shah |
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Automated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B / Chabitha Devarj in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
[article]
Titre : Automated geometric correction of multispectral images from high resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B Type de document : Article/Communication Auteurs : Chabitha Devarj, Auteur ; Chintan A. Shah, Auteur Année de publication : 2014 Article en page(s) : pp 13 - 24 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chambre DTC
[Termes IGN] correction géométrique
[Termes IGN] géoréférencement direct
[Termes IGN] image à haute résolution
[Termes IGN] image CBERS
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] orthorectificationRésumé : (Auteur) China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS-2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Numéro de notice : A2014-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33026
in ISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 13 - 24[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Unsupervised classification of hyperspectral data: an ICA mixture model based approach / Chintan A. Shah in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)
[article]
Titre : Unsupervised classification of hyperspectral data: an ICA mixture model based approach Type de document : Article/Communication Auteurs : Chintan A. Shah, Auteur ; M.K. Arora, Auteur ; P.K. Varshney, Auteur Année de publication : 2004 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification non dirigée
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Conventional unsupervised classification algorithms that model the data in each class with a multivariate Gaussian distribution are often inappropriate, as this assumption is frequently not satisfied by the remote sensing data. In this Letter, a new algorithm based on independent component analysis (ICA) is presented. The ICA mixture model (ICAMM) algorithm that models class distributions as non-Gaussian densities has been employed for unsupervised classification of a test image from the AVIRIS sensor. A number of feature-extraction techniques have also been examined that serve as a preprocessing step to reduce the dimensionality of the hyperspectral data. The proposed ICAMM algorithm results in significant increase in the classification accuracy over that obtained from the conventional K-means algorithm for land cover classification. Numéro de notice : A2004-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160310001618040 En ligne : https://doi.org/10.1080/01431160310001618040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26588
in International Journal of Remote Sensing IJRS > vol 25 n° 2 (January 2004)[article]Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 080-04021 RAB Revue Centre de documentation En réserve L003 Exclu du prêt