Integrating Weighted Feature Fusion and the Spatial Attention Module with Convolutional Neural Networks for Automatic Aircraft Detection from SAR Images

Jielan Wang,Zhouhao Pan,Hongguang Xiao,Lifu Chen,Xingmin Cai,Ru Luo,Jin Xing

doi:10.3390/rs13050910

Abstract

The automatic detection of aircrafts from SAR images is widely applied in both military and civil fields, but there are still considerable challenges. To address the high variety of aircraft sizes and complex background information in SAR images, a new fast detection framework based on convolution neural networks is proposed, which achieves automatic and rapid detection of aircraft with high accuracy. First, the airport runway areas are detected to generate the airport runway mask and rectangular contour of the whole airport are generated. Then, a new deep neural network proposed in this paper, named Efficient Weighted Feature Fusion and Attention Network (EWFAN), is used to detect aircrafts. EWFAN integrates the weighted feature fusion module, the spatial attention mechanism, and the CIF loss function. EWFAN can effectively reduce the interference of negative samples and enhance feature extraction, thereby significantly improving the detection accuracy. Finally, the airport runway mask is applied to the detected results to reduce false alarms and produce the final aircraft detection results. To evaluate the performance of the proposed framework, large-scale Gaofen-3 SAR images with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EWFAN algorithm are 95.4% and 3.3%, respectively, which outperforms Efficientdet and YOLOv4. In addition, the average test time with the proposed framework is only 15.40 s, indicating satisfying efficiency of automatic aircraft detection.

Highlights

Synthetic Aperture Radar (SAR) is an advanced active microwave earth observation approach, which is insensitive to clouds and fogs, and images the earth surface all day and all night
Coordinate mapping was utilized to obtain accurate airport detection results from high-resolution SAR images. This method presented high detection accuracy, and greatly reduced the training and testing time compared to Aiming at the problems of low automation of existing SAR image aircraft detection algorithms, long airport extraction time, and complex preprocessing procedures, this paper proposes an efficient and automatic aircrafts detection framework
The detection rate represents the ratio of the number of aircraft targets correctly detected by the network (C) [5] to the number of aircraft targets in the label (L), and the false alarm rate is the ratio of the number of false alarms(they are not aircrafts, but they have been detected as aircrafts) to the number of prediction boxes last output by the network (S), and the missed detection rate is the ratio of the number of missed alarms to the number of aircraft targets in the label

Summary

Introduction

Synthetic Aperture Radar (SAR) is an advanced active microwave earth observation approach, which is insensitive to clouds and fogs, and images the earth surface all day and all night. SAR turns out to be fascinating for reconnaissance missions under various weather conditions. The resolution of SAR platforms can reach the centimeterlevel, which offers opportunities to identify detailed targets in various application domains. Since 1978, SAR has attracted considerable attention of the radar scientific community because of its unique imaging mechanism; and it has been widely used in both military and civilian fields. The detection and identification of aircraft are essential to the effective management of the airport. Type, location, and status information of aircrafts is Remote Sens.

Methods

Results

Discussion

Conclusion