LR Aerial Photo Categorization by Cross-Resolution Perceptual Knowledge Propagation

There are hundreds of high- and low-altitude earth observation satellites that asynchronously capture massive-scale aerial photographs every day. Generally, high-altitude satellites take low-resolution (LR) aerial pictures, each covering a considerably large area. In contrast, low-altitude satellites capture high-resolution (HR) aerial photos, each depicting a relatively small area. Accurately discovering the semantics of LR aerial photos is an indispensable technique in computer vision. Nevertheless, it is also a challenging task due to: 1) the difficulty to characterize human hierarchical visual perception and 2) the intolerable human resources to label sufficient training data. To handle these problems, a novel cross-resolution perceptual knowledge propagation (CPKP) framework is proposed, focusing on adapting the visual perceptual experiences deeply learned from HR aerial photos to categorize LR ones. Specifically, by mimicking the human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually/semantically salient foreground regions coupled with the background nonsalient regions. This model can: 1) produce a gaze-shifting path (GSP) simulating human gaze behavior and 2) engineer the deep feature for each GSP. Afterward, a kernel-induced feature selection (FS) algorithm is formulated to obtain a succinct set of deep GSP features discriminative across LR and HR aerial photos. Based on the selected features, the labels from LR and HR aerial photos are collaboratively utilized to train a linear classifier for categorizing LR ones. It is worth emphasizing that, such a CPKP mechanism can effectively optimize the linear classifier training, as labels of HR aerial photos are acquired more conveniently in practice. Comprehensive visualization results and comparative study have validated the superiority of our approach.