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Veuillez utiliser cette adresse pour citer ce document : http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6168

Titre: Sift and Gabor Features for Very High Resolution Image Classification
Auteur(s): Fiala, C.
Daamouche, Abdelhamid
Mots-clés: 2-D GABOR FILTER
KNN
SIFT
SVM
VHR IMAGES
Date de publication: 2020
Editeur: Institute of Electrical and Electronics Engineers
Collection/Numéro: 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020 - Proceedings March 2020, Article number 9105324, Pages 156-159 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2020;Tunis; Tunisia; 9 March 2020 through 11 March 2020; Category numberCFP20T63-ART; Code 160621;
Résumé: this paper presents a new approach to extract features from high resolution images inspired by the sift descriptor and gabor features. both of these two methods are powerful when used separately or together in region-based or pixel-based classification, they brought a high accuracy. our approach was applied to classify two very high resolution images of boumerdes (algeria) and djeddah (ksa) using knn and svm. the obtained results achieved promising performance compared to using spectral information alone
URI/URL: https://www.scopus.com/record/display.uri?eid=2-s2.0-85086720698&origin=SingleRecordEmailAlert&dgcid=raven_sc_affil_en_us_email&txGid=cd8618ad6784d3047893f91cbc82b061
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/6168
ISBN: 978-172812190-1
Collection(s) :Communications Internationales

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