A hierarchical boosting algorithm based on feature selection for Synthetic Aperture Radar image retrieval

A hierarchical boosting algorithm based on feature selection is proposed for Synthetic Aperture Radar (SAR) image retrieval here. Motivated by Joint Boost and Shared feature frameworks, category combinations are adopted as the training and classification set of a hierarchical boosting-based classification framework’s middle layer. It has superiorities over the classical method which combines Boosting algorithm with many features as inputs. Meanwhile, different from the Joint Boost scheme, our method separates feature selection from training and retrieval processes. Thus more flexible feature selecting schemes can be used, e.g. nonlinear separating plane can be obtained. Some typical features such as Gabor, Edge Orientation Histogram, gray-level co-occurrence matrix, Grey Histogram and Tamura are used as the candidates of the input and statistics-based selecting method is used as the feature selection scheme. The experiments are carried on the KTH_TIPS and SAR image datasets and the results reveal our algorithm’s efficient performances and superiorities.