Local query on satellite images based on interest points

Research concerning the detection of interest points features for local query is particularly rich and many methods have been proposed in the literature. Matching of interest points is an essential step in the interest point detectors evaluation process. There are many existing methods for interest points matching and most of them are related to the detectors parameters. In this paper, we present a new matching method based on the prediction validation principle by matching interest points with a local description and with adding spatial constraints. Harris [4], SIFT [6] and SURF [1] were used to detect interest-point features from satellite images. This paper presents a short evaluation of these detectors on satellite images with different spatial resolutions, describes the experimental setup and the results of the detected interest points used for the comparison of the detection rate and the repeatability. Finally, we determine which detector leads to the best results for local query on satellite images.