Towards Domain Adaptive Vehicle Detection in Satellite Image by Supervised Super-Resolution Transfer

Vehicle detection in satellite image has attracted extensive research attentions with various emerging applications. However, the detector performance has been significantly degenerated due to the low resolutions of satellite images, as well as the limited training data. In this paper, a robust domain-adaptive vehicle detection framework is proposed to bypass both problems. Our innovation is to transfer the detector learning to the high-resolution aerial image domain, where rich supervision exists and robust detectors can be trained. To this end, we first propose a super-resolution algorithm using coupled dictionary learning to "augment" the satellite image region being tested into the aerial domain. Notably, linear detection loss is embedded into the dictionary learning, which enforces the augmented region to be sensitive to the subsequent detector training. Second, to cope with the domain changes, we propose an instance-wised detection using Exemplar Support Vector Machines (E-SVMs), which well handles the intra-class and imaging variations like scales, rotations, and occlusions. With comprehensive experiments on large-scale satellite image collections, we demonstrate that the proposed framework can significantly boost the detection accuracy over several state-of-the-arts.