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Proceedings Paper

High resolution satellite image indexing and retrieval using SURF features and bag of visual words
Author(s): Samia Bouteldja; Assia Kourgli
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Paper Abstract

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034120 (17 March 2017); doi: 10.1117/12.2268803
Show Author Affiliations
Samia Bouteldja, Univ. des Sciences et de la Technologie Houari Boumediene (Algeria)
Assia Kourgli, Univ. des Sciences et de la Technologie Houari Boumediene (Algeria)


Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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