A novel SAR target detection algorithm via multi-scale SIFT features

A detection method for SAR targets based on extraction and dimensionality reduction of multi-scale SIFT features is proposed. Aiming at the problem that SAR target features cannot be completely described in single scale, we put Gaussian scale space and multi-group of seed points into use to achieve the extraction of multi-scale SIFT features. Meanwhile, there are description redundancies and structural redundancies in the same and different scales, so the method of sparse coding and features statistics is introduced to reduce redundancies and dimensionality for feature vectors. Through quantitative analysis, the most optimal parameters of multi-scale factor and number are fixed, this makes the target features contain both the overall target contour information and the image details. Comparison with traditional target detectors, such as CFAR, SIFT features and multi-scale SIFT-PCA features etc, is performed in detail. The experimental results and their analysis show preliminarily the superiorities of the proposal.