Aircraft Detection System Based on Regions with Convolutional Neural Networks

Abstract

Object detection in remote sensing imagery is an important topic of image processing researches. Detection of objects and regions from satellite imagery takes its place in various applications such as the detection of residential areas and agricultural lands, road lines, ships, as well as airport and hangar detections. As a more specific remote sensing imagery based object-detection process, a stationary aircraft detection system could serve as a model in some military applications. Such a model could serve the detection of stationary aircraft targets in airports. In the proposed study, a deep learning-based model detects the aircraft in the airports using the satellite images from Google Earth. The deep learning model uses the state of the art Regions with Convolutional Neural Network (RCNN). Firstly a built from scratch CNN design is used for the basic learning step of the system. Then, RCNN performs region detection, which anchors the stationary aircraft object bounding boxes. A large dataset containing aircraft images is preferred for the training of CNN. To validate the system, satellite images captured from airports in Turkey are used. The results of the study show that the proposed model successfully operates aircraft detection with high-performance rates. While the classifier network structure, which constitutes the first step of the study, produces 98.4% test accuracy, the proposed aircraft detection framework has successfully performed the aircraft identification process by producing matched bounding boxes in the test images.