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

Abstract

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.

Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery

“…But these methods are all examples of applying deep learning to extract airports from optical remote sensing images, and deep learning often tends to overfit due to the training with specific airport datasets. Aiming at detecting airport from high-resolution SAR images, Chen et al [1] proposed a deep learning network Multi-level and Densely Dual Attention (MDDA) to extract runway areas. It could achieve high-precision airport extraction, but the network required a great quantity of high-quality labeled datasets and long training.…”

“…In 2017, DeepLabv3 [27] improved ASPP based on DeepLabv2, to achieve better overall performance in object detection. In 2018, DeepLabv3+ [1] introduced the encoder and decoder modules, designed an effective decoder module, and incurred the depth-wise separable convolution as well. It enabled the model to effectively reduce the amount of calculations and parameters while maintaining satisfactory performance.…”

“…In recent years, deep learning has developed rapidly, and some preliminary results have been achieved in the field of SAR image airport extraction [1]. But at present, how to use deep learning network to extract more effective airport features needs further research.…”

“…As important transportation hubs and military facilities, the detection of airports from Synthetic Aperture Radar (SAR) images attracts considerable interests for a long time [1]. SAR [2] provides all-weather and all-day observation and possesses the ability of cloud and fog penetration.…”

“…However, SAR images are more difficult to interpret than optical images, since the analysis of SAR images is usually more complicated. Previously, most of airports detection is based on optical remote sensing images, which is time-consuming and labor-intensive [1].…”