Mental Retrieval of Large-Scale Satellite Images Via Learned Sketch-Image Deep Features

Searching targets of interest in large-scale satellite images is an imperative task, which becomes a challenging issue when the targets reside only in the mind of the user as a set of subjective visual patterns. In this paper, we take the advantage of hand-drawn sketches' strong intuition of describing mental target to address the problem of no available exemplar query. We introduce a multi-level-of-detail model to learn a cross-domain representation for bridging the gap between sketches and satellite images. To train the model, we propose a novel method of generating satellite images with corresponding level of details based on generative adversarial network. Experiments on both large-scale satellite images and commonly used RS datasets demonstrate the effectiveness and superiority of our method.