17 March 2017 High resolution satellite image indexing and retrieval using SURF features and bag of visual words
Author Affiliations +
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Samia Bouteldja, 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); doi: 10.1117/12.2268803; https://doi.org/10.1117/12.2268803
PROCEEDINGS
5 PAGES


SHARE
Back to Top