Sketch-based aerial image retrieval

Notwithstanding aerial image retrieval is an important and obligatory task, existing retrieval systems lose their efficiency when there is no available aerial image used as the exemplar query. In this paper, we take free-hand sketches into consideration and address the problem of sketch-based aerial image retrieval. This is an extremely challenging task due to the complex surface structures and huge variations of resolutions of aerial images, and few works have been devoted to it. For the first time to our knowledge, we propose a framework to bridge the gap between sketches and aerial images. Specifically, an aerial sketch-image dataset is first collected. Sketches and aerial images are augmented to varied levels of details and used to train a multi-scale deep hierarchical model. The fully-connected layers of the deep model are used as cross-domain features, and the similarity between aerial images and sketches is measured by the Euclidean distance. Experiments on several public aerial image datasets demonstrate the efficiency and superiority of the proposed method.