Hierarchical object detection for very high-resolution satellite images

Object detection from satellite images is challenging and either computationally expensive or labor intense. Satellite images often cover large areas of more than 10km x 10km. They include objects of different scales, which makes it hard to detect all of them at the same image resolution. Considering that airplanes are usually located in airports, ships are often distributed in ports and sea areas, and that oil depots are typically found close to airports or ports, we propose a new hierarchical object detection framework for very high-resolution satellite images. Our framework prescribes two stages: (1) detecting airports and ports in down-sampled satellite images and (2) mapping the detected object back to the original high-resolution satellite images for detecting the smaller objects near them. In order to improve the efficiency of object detection, we further propose a contextual information based deep feature extraction approach for both of the hierarchical detection steps, as well as an inclined bounding box based arbitrarily-oriented object location mechanism suitable especially for the smaller objects. Comprehensive experiments on a public dataset and two self-assembled datasets (which we made publicly available) show the superior performance of our method compared to standalone state-of-the-art object detectors. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Object detection from satellite images is challenging due to large coverage areas and objects of different scales. A hierarchical framework with deep feature extraction and inclined bounding box mechanism is proposed to improve efficiency. Experimental results demonstrate the superior performance of the proposed method compared to standalone state-of-the-art object detectors.