  Driven by the urgent demand for managing remote sensing big data, large-scale
remote sensing image retrieval (RSIR) attracts increasing attention in the
remote sensing field. In general, existing retrieval methods can be regarded as
visual-based retrieval approaches which search and return a set of similar
images from a database to a given query image. Although retrieval methods have
achieved great success, there is still a question that needs to be responded
to: Can we obtain the accurate semantic labels of the returned similar images
to further help analyzing and processing imagery? Inspired by the above
question, in this paper, we redefine the image retrieval problem as visual and
semantic retrieval of images. Specifically, we propose a novel deep hashing
convolutional neural network (DHCNN) to simultaneously retrieve the similar
images and classify their semantic labels in a unified framework. In more
detail, a convolutional neural network (CNN) is used to extract
high-dimensional deep features. Then, a hash layer is perfectly inserted into
the 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 to
generate class distribution. Finally, a loss function is elaborately designed
to simultaneously consider the label loss of each image and similarity loss of
pairs of images. Experimental results on two remote sensing datasets
demonstrate that the proposed method achieves the state-of-art retrieval and
classification performance.
