Airport detection in remote sensing real-open world using deep learning

Abstract

Remote sensing real-open world of large-scare areas brings a high false alarm rate to object detection because of highly complex backgrounds. In this study, we constructed a two-stage extraction framework candidate region extraction (CRE)–multi-core binary analysis (MCBA) (CRE-MCBA) to improve the correct detection rate (DR) and reduce the error DR for airport extraction in large-scale remote sensing real-open areas. First, global sample labeling and large-scale runway CRE were conducted. Open-sourced data were applied to match the detection results spatially, and the MCBA was built for the issue of unbalanced positive and negative samples to mine potential airports. The minimum penalty term δ was also introduced into focal loss to improve detection ability in a remote sensing real-open world area. In the 219,041 km 2 study area at the Yangtze River Delta in China, the detection and error reduction rates were 100% and 97.3%, respectively. A total of 37 airports with prominent runway characteristics were detected, with 9 newly added airports. We also test the CRE-MCBA framework in Japan, Korean Peninsula, and Madhya Pradesh of India. Compared with other detection methods, ours has more robust regional adaptability and generalization ability and realizes the practical mining of potential objects.