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Open AccessArticle

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

by Lifu Chen 1,2, Siyu Tan 1,2, Zhouhao Pan 3, Jin Xing 4, Zhihui Yuan 1,2,*, Xuemin Xing 2,5 and Peng Zhang 1,2
1
School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China
2
Laboratory of Radar Remote Sensing Applications, Changsha University of Science & Technology, Changsha 410014, China
3
China Academy of Electronics and Information Technology, Beijing 100041, China
4
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
5
School of Traffic & Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 560; https://doi.org/10.3390/rs12030560 (registering DOI)
Received: 31 December 2019 / Revised: 3 February 2020 / Accepted: 5 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Deep Learning Approaches for Urban Sensing Data Analytics)
The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy. View Full-Text
Keywords: deep learning; airport extraction; deep neural network; dense connection; attention mechanism; synthetic aperture radar deep learning; airport extraction; deep neural network; dense connection; attention mechanism; synthetic aperture radar
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Chen, L.; Tan, S.; Pan, Z.; Xing, J.; Yuan, Z.; Xing, X.; Zhang, P. A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism. Remote Sens. 2020, 12, 560.

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