Abstract
Abstract. Due to the diverse structure and complex background of airports, fast and accurate airport detection in remote sensing images is challenging. Currently, airport detection method is mostly based on boxes, but pixel-based detection method which identifies airport runway outline has been merely reported. In this paper, a framework using deep convolutional neural network is proposed to accurately identify runway contour from high resolution remote sensing images. Firstly, we make a large and medium airport runway semantic segmentation data set (excluding the south Korean region) including 1,464 airport runways. Then DeepLabv3 semantic segmentation network with cross-entropy loss is trained using airport runway dataset. After the training using cross-entropy loss, lovasz-softmax loss function is used to train network and improve the intersection-over-union (IoU) score by 5.9%. The IoU score 0.75 is selected as the threshold of whether the runway is detected and we get accuracy and recall are 96.64% and 94.32% respectively. Compared with the state-of-the-art method, our method improves 1.3% and 1.6% of accuracy and recall respectively. We extract the number of airport runway as well as their basic contours of all the Korean large and medium airports from the remote sensing images across South Korea. The results show that our method can effectively detect the runway contour from the remote sensing images of a large range of complex scenes, and can provide a reference for the detection of the airport.
Highlights
Remote sensing image object detection technology has attracted massive attention, especially in the fields of urban management, agriculture and military
We propose a framework that the precise outline of airport runway can be identified from remote sensing images using DeepLabv3
The result of airport runway IoU in the validation set is shown in Table 4, which depicts that the use of lovasz-softmax loss improves the accuracy of the results by nearly 6% compared with that using cross entropy loss
Summary
Remote sensing image object detection technology has attracted massive attention, especially in the fields of urban management, agriculture and military. As one of the most important facilities, the accurate detection of airports has attracted widespread concerns. It is challenging to accurately detect airport in remote sensing images with the diverse structure and complex background. Due to the characteristics of large aspect ratio and internal gray uniformity of the airport, the runway is the most discriminating feature of airport. Many methods have been proposed based on airport runway to detect airport from remote sensing images in recent years. According to the characteristics used, they can be divided into two categories: 1. Using features designed by prior knowledge to extract airports(Tang, 2015; Zhu, 2015), 2. Using features automatically extracted from convolutional neural networks(Xiao, 2017; Zhu, 2018) According to the characteristics used, they can be divided into two categories: 1. using features designed by prior knowledge to extract airports(Tang, 2015; Zhu, 2015), 2. using features automatically extracted from convolutional neural networks(Xiao, 2017; Zhu, 2018)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
