Aircraft detection in high-resolution synthetic aperture radar images based on optical training dataset

Synthetic Aperture Radar (SAR) emits microwave electromagnetic pulses and detects remote targets through the backscattered echo signals. With the continuous advancement of technology, high-resolution SAR imaging can accurately observe various targets with small sizes, dense connections, and diverse shapes. The steep terrain or tall objects in SAR images display prominent shadows due the obstruction of ground-facing electromagnetic waves resulting in weak echo signals in the shadowed areas. Small targets can generate discrete shadows with contours similar to optical contours in high-resolution SAR images when the radar observing angle is appropriate. This characteristic, which shares similarities with the target contour, can be utilized to build the correlation between SAR images and other modal images such as optical images. This study uses aircraft as an example to validate the feasibility of this approach. It trains the YOLOv5 object detection network on the Remote Sensing Object Detection (RSOD) dataset of optical airport images, which is then utilized to detect the shadows of aircraft in high-resolution SAR images, indirectly achieving aircraft detection. <br/> <br/> Firstly, the shadows in SAR images have an inverse relationship with the signal of the aircraft body in optical images in terms of grayscale. Therefore, it is possible to simply invert the grayscale of one of them. In this study, SAR images were chosen for grayscale inversion. After a simple grayscale inversion, the network detected a significant number of aircraft in the image at a confidence level of 0.2, while only part of aircraft were detected in the image without inversion. Besides, a series of adjustments were made to the brightness and contrast of the grayscale inverted image in order to find the optimal setting for aircraft detection. After scanning and adjusting the brightness and contrast, the network detects a certain number of additional aircraft in the grayscale inverted images at a confidence level of 0.2. The maximum number of aircraft detections was achieved at a specific filtering spatial frequency after applying filtering with different spatial frequencies. The overall detection result achieved an accuracy of over 80%.The maximum number of aircraft detections was achieved at a specific filtering spatial frequency after applying filtering with different spatial frequencies. The overall detection result achieved an accuracy of over 80%.