Content-Based Remote Sensing Image Retrieval Based on Fuzzy Rules and a Fuzzy Distance

Abstract

The methods in remote sensing image retrieval (RSIR) usually search the whole retrieval data set in the retrieval process, which takes much time and is unnecessary. To reduce the overall search time, this letter proposes a new retrieval scheme based on fuzzy rules. The proposed method calculates the fuzzy class membership of images using two ways. The first way predicts the fuzzy class membership by convolutional neural network (CNN). The other uses the image-to-class distance that is a distance between an image and each class on the training data set. The two fuzzy class memberships are used to measure the classification confidence, and a query image is classified into three fuzzy sets, namely, “low classification confidence,” “medium classification confidence,” and “high classification confidence,” based on the classification confidence. The fuzzy rules are built according to fuzzy classification to choose the search space for each fuzzy set. The final search space is determined by the two search spaces obtained by fuzzy rules. Moreover, the fuzzy distance between a query image and a retrieved image is used to improve the retrieval performance, which is calculated according to their fuzzy class memberships and the Euclidean distance between the two images. The experimental results on University of California, Merced data set (UCMD) and PatternNet databases show that our proposed method can not only enhance the retrieval performance but also reduce the search time in comparison to other state-of-the-art techniques.