LR Aerial Photo Categorization by Semi-Supervised Perceptual Feature Selection

Abstract

Recognizing the semantic categories of low-resolution (LR) aerial photos is an indispensable technique in geoscience and remote sensing. However, it is also a challenging task in practice. In this work, a semi-supervised perceptual feature selection (SPFS) pipeline is proposed for LR aerial photo categorization, focusing on selecting high quality perception-guided visual features. Specifically, by mimicking human vision system, a novel low-rank model is designed to decompose each LR aerial photo into multiple visually or semantically salient foreground regions coupled with the background non-salient regions. This model can 1) produce the a gaze shifting path (GSP) simulating human gaze behavior, and 2) generate hierarchical deep representation for a GSP. Afterward, a semi-supervised feature selection (FS) is leveraged toward a succinct set of discriminative deep GSP features, wherein only labels of LR aerial photos are required. Based on the selected features, a classifier is trained for visual categorization. Comprehensive experimental results have validated our method's advantage.