Query-Adaptive Remote Sensing Image Retrieval Based on Image Rank Similarity and Image-to-Query Class Similarity

Abstract

Many image features have been proposed for image retrieval; hence, effectively fusing these features to alleviate the large variation in performance among image queries when using single image features has become a major challenge in remote sensing (RS) image retrieval. Because high-resolution remote sensing images have abundant and complex visual contents, accurately measuring the similarity between two images is another important problem. To address these challenges, we propose a novel RS image retrieval method that uses query-adaptive feature weights to fuse features and utilizes two image similarities to improve retrieval performance. First, we use the image rank similarity, which measures the similarity between two images according to their corresponding top-m image lists from a reference image collection, to calculate the similarity of each feature between a query image and each retrieved image. Then, we assign a weight to each feature to fuse these features via our query-adaptive weighting method. Finally, we take the query image and its neighborhood set selected from the retrieval dataset as the query class and utilize the image-to-query class similarity to re-rank the retrieval results. Extensive experiments are conducted on two publicly available RS image databases. Compared with the state-of-the-art methods, the proposed method can significantly enhance the retrieval precision.