An Improved Framework for Airport Detection Under the Complex and Wide Background

As an important transportation facility, airports substantially affect the economic lives of people. However, the full extraction of airports located in a vast area is concerning. The size of an airport in a previous wide area detection framework is relatively large and has a strong saliency in remote sensing images, whereas the contradiction between a complex geographical background and a small airport size has yet to be resolved. In this study, we propose a set of automatic detection frameworks to realize efficient detection for various airports in nine Indian states/union territories under the condition that only runway samples are labeled. Preliminary extraction of runway features is performed with a high F1 and recall rate, and teacher nodes judge and guide the results. Next, the output is connected to classification and segmentation for outlier elimination and pixel extraction to locate the runways. For the study area, the proposed framework airport retention rate (RR) is 92.7%, with the false alarm reduction rate (FARR) reduced by a maximum of 95.3%. A total of 192 airports are discovered, and the effective airport growth rate (GR) is 47.4%. Compared to previous work, RR, GR, FARR, and run efficiency increased by 2.2%, 16.0%, 4.5%, and 432.5%, respectively, with more small- and medium-sized airports detected. Furthermore, the framework is tested in Japan, and 155 airports are detected. Thus, the proposed framework effectively improves detection capability for small- and medium-sized airports in large-scale areas and updates the airport database.