SAR image retrieval based-on fly algorithm

Abstract

SAR (Synthetic Aperture Radar) image retrieval, of which the crucial problem is how to search and organize SAR images effectively, has long been a hotspot in SAR-related researches with the increasing amount of SAR images. Besides, SAR image retrieval is also more challenging compared with normal optical image retrieval due to its complicated imaging mechanism. Many SAR image retrieval methods use the high-dimensional feature to improve accuracy, which brings a high computational cost and storage cost for large scale search. In this paper, a system based on fly algorithm for SAR image retrieval is presented. The fly algorithm can generate hash codes for images effectively by mimicing the fruit fly olfactory circuit, which can greatly reduce the dimension of feature space. Three computational strategies extracted from the logic of an important sensory function are used in the fly algorithm to improve the performance of computational similarity searches. Moreover, another effective binary quantization method, different from the quantization method in fly algorithm, is adopted to generate binary hash code in SAR image retrieval system. Experiment results on two representative SAR image datasets, MSTAR and OpenSAR show that the new method based-on fly algorithm outperform other state-of-the-art SAR image retrieval methods.