Fusion Similarity-Based Re-ranking for SAR Image Retrieval

Abstract

A new reranking method, fusion similarity-based reranking, is proposed in this letter to improve the performance of synthetic aperture radar (SAR) image retrieval. First, the top ranked SAR images within the initial retrieval results are picked for reranking. Considering the negative influence of the speckle noise, three SAR-oriented visual features are selected to represent them. In addition, the different relevance scores corresponding to an SAR image are estimated in various modalities (i.e., different feature spaces). Second, a fusion similarity is defined under the relevance score space to measure the resemblance between two SAR images. This fusion similarity is calculated using the modalimage matrix, which is construed by the estimated scores to integrate the contributions of all modalities. Finally, an existing reranking function is adopted to rerank the SAR images with the help of the estimated scores and calculated fusion similarities. The positive experimental results demonstrate that our reranking method is effective and efficient.

Existing System

Proposed System

Architecture