Airport Detection-Based Cosaliency on Remote Sensing Images

This paper proposes a contour extraction model based on cosaliency detection for remote sensing image airport detection and improves the traditional line segmentation detection (LSD) algorithm to make it more suitable for the goal of this paper. Our model consists of two parts, a cosaliency detection module and a contour extraction module. In the first part, the cosaliency detection module mainly uses the network framework of Visual Geometry Group-19 (VGG-19) to obtain the result maps of the interimage comparison and the intraimage consistency, and then the two result maps are multiplied pixel by pixel to obtain the cosaliency mask. In the second part, the contour extraction module uses superpixel segmentation and parallel line segment detection (PLSD) to refine the airport contour and runway information to obtain the preprocessed result map, and then we merge the result of cosaliency detection with the preprocessed result to obtain the final airport contour. We compared the model proposed in this article with four commonly used methods. The experimental results show that the accuracy of the model is 15% higher than that of the target detection result based on the saliency model, and the accuracy of the active contour model based on the saliency analysis is improved by 1%. This shows that the model proposed in this paper can extract a contour that closely matches the actual target.

AIRPORT RUNWAY SEMANTIC SEGMENTATION BASED ON DCNN IN HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGES

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.