Semi-Supervised Perception Augmentation for Aerial Photo Topologies Understanding.

Abstract

Intelligently understanding the sophisticated topological structures from aerial photographs is a useful technique in aerial image analysis. Conventional methods cannot fulfill this task due to the following challenges: 1) the topology number of an aerial photo increases exponentially with the topology size, which requires a fine-grained visual descriptor to discriminatively represent each topology; 2) identifying visually/semantically salient topologies within each aerial photo in a weakly-labeled context, owing to the unaffordable human resources required for pixel-level annotation; and 3) designing a cross-domain knowledge transferal module to augment aerial photo perception, since multi-resolution aerial photos are taken asynchronistically in practice. To handle the above problems, we propose a unified framework to understand aerial photo topologies, focusing on representing each aerial photo by a set of visually/semantically salient topologies based on human visual perception and further employing them for visual categorization. Specifically, we first extract multiple atomic regions from each aerial photo, and thereby graphlets are built to capture the each aerial photo topologically. Then, a weakly-supervised ranking algorithm selects a few semantically salient graphlets by seamlessly encoding multiple image-level attributes. Toward a visualizable and perception-aware framework, we construct gaze shifting path (GSP) by linking the top-ranking graphlets. Finally, we derive the deep GSP representation, and formulate a semi-supervised and cross-domain SVM to partition each aerial photo into multiple categories. The SVM utilizes the global composition from low-resolution counterparts to enhance the deep GSP features from high-resolution aerial photos which are partially-annotated. Extensive visualization results and categorization performance comparisons have demonstrated the competitiveness of our approach.