A Survey of Deep Learning-Based Object Detection Methods and Datasets for Overhead Imagery

Abstract

Significant advancements and progress made in recent computer vision research enable more effective processing of various objects in high-resolution overhead imagery obtained by various sources from drones, airplanes, and satellites. In particular, overhead images combined with computer vision allow many real-world uses for economic, commercial, and humanitarian purposes, including assessing economic impact from access crop yields, financial supply chain prediction for company’s revenue management, and rapid disaster surveillance system (wildfire alarms, rising sea levels, weather forecast). Likewise, object detection in overhead images provides insight for use in many real-world applications yet is still challenging because of substantial image volumes, inconsistent image resolution, small-sized objects, highly complex backgrounds, and nonuniform object classes. Although extensive studies in deep learning-based object detection have achieved remarkable performance and success, they are still ineffective yielding a low detection performance, due to the underlying difficulties in overhead images. Thus, high-performing object detection in overhead images is an active research field to overcome such difficulties. This survey paper provides a comprehensive overview and comparative reviews on the most up-to-date deep learning-based object detection in overhead images. Especially, our work can shed light on capturing the most recent advancements of object detection methods in overhead images and the introduction of overhead datasets that have not been comprehensively surveyed before.

Keywords

• Object detection
• satellites
• synthetic aperture radar
• unmanned aerial vehicles