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, Samia Bouteldja, Assia Kourgli, 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