Cross-Resolution Perceptual Knowledge Propagation for Low-Resolution Aerial Photograph Categorization

Accurately recognizing the semantic categories of LR (low-resolution) aerial photos is an indispensable technique in remote sensing. In practice, however, this task is nontrivial due to: 1) the difficulty to encode human visual perception in the recognition process, 2) the intolerable human resources to label sufficient training LR aerial photos, and 3) the challenge to select high quality features for categorization. To handle these problems, a novel cross-resolution perceptual knowledge propagation (CPKP) is proposed, focusing on leveraging the visual perceptual experiences deeply learned from HR (high-resolution) aerial photos to enhance categorizing LR ones. Specifically, by mimicking human vision system, a novel low-rank model is proposed to decompose each LR aerial photo into multiple visually/semantically salient foreground regions coupled with the background non-salient ones. This model can 1) produce the gaze shifting path (GSP) simulating human gaze shifting sequence, and 2) calculate 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 these, the labels from LR and HR aerial photos are collaboratively utilized to train a linear classifier for categorizing LR ones. Noticeably, our CPKP framework can effectively optimize the linear classifier training. This is because labels of HR aerial photos can be acquired more conveniently than LR ones practically. Comprehensive visualization results and comparative study have validated the superiority of our method.