Cross-domain remote sensing image retrieval with Gabor- based CNN

Domain adaptation is the ability to improve the learning efficiency of the target domain by using the prior knowledge of the source domain. When applied to new tasks in the target domain, the performance of domain adaptation trained in the source domain declines sharply. The purpose is to realize a new retrieval task in the target domain using only a small number of labelled data samples, with the aid of the prior knowledge learned in the source domain. This paper focuses on semi-supervised domain adaptation in remote sensing image retrieval. The contributions of this paper are threefold. First, we construct Gabor-based CNNs to facilitate the networks to effectively capture the texture information of images. Second, we propose a cross-domain knowledge transfer strategy based on dual Gabor neural network learning. Third, we propose an unsupervised random feature mapping method based on probability distance. A large number of experiments have been conducted on UCM, WHU-RS, RSSCN7, AID, and PatternNet datasets. The results show that this method greatly improves the retrieval accuracy on the target domain and obtains state-of-the-art retrieval accuracy. The source code is available at http://nave.vr3i.com.