SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information
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| Titre | SVM-based classification of High resolution Urban Satellites Images using Dense SURF and Spectral Information |
| Type de publication | Conference Paper |
| Year of Publication | 2018 |
| Auteurs | Anzid H, Le Goic G, Bekkari A, Mansouri A, Mammass D |
| Conference Name | PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS: THEORIES AND APPLICATIONS (SITA'18) |
| Publisher | ASSOC COMPUTING MACHINERY |
| Conference Location | 1515 BROADWAY, NEW YORK, NY 10036-9998 USA |
| ISBN Number | 978-1-4503-6462-1 |
| Mots-clés | Cielab space, dense SURF, remote sensing images, Spectral information, SVM classification |
| Résumé | Remote-sensing focusing on image classification knows a large progress and receives the attention of the remote-sensing community day by day. Combining many kinds of extracted features has been successfully applied to High resolution urban satellite images using support vector machine (SVM). In this paper, we present a methodology that is promoting a performed classification by using pixel-wise SURF description features combined with spectral information in Cielab space for the first time on common scenes of urban imagery. The proposed method gives a promising classification accuracy when compared with the two types of features used separately. |
| DOI | 10.1145/3289402.3289502 |