Geometry-preserving Perceptual Feature Selection for Categorizing LR Aerial Photographs

There are plenty of high- and low-altitude earth observation satellites asynchronously capture massive-scale aerial photographs everyday. In practice, high-altitude satellites take low-resolution (LR) aerial pictures, each covers a considerably large area. Comparatively, low-altitude satellites capture high-resolution (HR) aerial photos, each depicts a relatively small area. Accurately mining the LR aerial images' semantic clues is a significant task in pattern recognition. However, it is also a challenging task due to: 1) the inefficiency to label adequate training samples, and 2) the difficulty to describe how humans preserving the world. To handle these problems, this work presents a so-called perceptual feature selector (GPFS) that optimally preserves samples' geometry, aiming at sufficiently discriminative perception-based representations to classify LR aerial photos. Particularly, by stimulating how humans sequentially perceiving different salient regions, we design a low-rank algorithm to divide an LR aerial image into a succinct set of attractive regions as well as a rich set of non-attractive regions. Such algorithm is able to: 1) generate a path well captures human gaze allocation, and 2) engineer the deeply-learned visual descriptor for the above gaze shifting path (GSP). Subsequently, the so-called GPFS is designed to obtain a set of high quality GSP features. GPFS is built upon a semi-supervised framework. Herein, we only require a small proportion of LR aerial images to be labeled. Besides, the labeled/unlabeled sample distribution are optimally preserved during feature selection (FS). Employing those refined features, we learn to categorize LR aerial photos. Plenty of empirical results shown the superiority of the proposed algorithm.