High resolution satellite image indexing and retrieval using SURF features and bag of visual words

Samia Bouteldja; Assia Kourgli

doi:10.1117/12.2268803

17 March 2017 High resolution satellite image indexing and retrieval using SURF features and bag of visual words

Samia Bouteldja, Assia Kourgli

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034120 (2017) https://doi.org/10.1117/12.2268803
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

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.

Citation Download Citation

Samia Bouteldja and Assia Kourgli "High resolution satellite image indexing and retrieval using SURF features and bag of visual words", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034120 (17 March 2017); https://doi.org/10.1117/12.2268803

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