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Driven by the urgent demand for managing remote sensing big data, large-scaleremote sensing image retrieval (RSIR) attracts increasing attention in theremote sensing field. In general, existing retrieval methods can be regarded asvisual-based retrieval approaches which search and return a set of similarimages from a database to a given query image. Although retrieval methods haveachieved great success, there is still a question that needs to be respondedto: Can we obtain the accurate semantic labels of the returned similar imagesto further help analyzing and processing imagery? Inspired by the abovequestion, in this paper, we redefine the image retrieval problem as visual andsemantic retrieval of images. Specifically, we propose a novel deep hashingconvolutional neural network (DHCNN) to simultaneously retrieve the similarimages and classify their semantic labels in a unified framework. In moredetail, a convolutional neural network (CNN) is used to extracthigh-dimensional deep features. Then, a hash layer is perfectly inserted intothe network to transfer the deep features into compact hash codes. In addition,a fully connected layer with a softmax function is performed on hash layer togenerate class distribution. Finally, a loss function is elaborately designedto simultaneously consider the label loss of each image and similarity loss ofpairs of images. Experimental results on two remote sensing datasetsdemonstrate that the proposed method achieves the state-of-art retrieval andclassification performance.

Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images