Special Section Guest Editorial: Synthetic Aperture Radar Imaging Technology in Deep Learning: New Trends and Viewpoints

Abstract

Deep learning has changed the way several synthetic aperture radar (SAR) image processing tasks are done. SAR images are used to discover and track ships, predict ocean waves, keep an eye on farmlands, help the military, and figure out how much damage has been done after a flood or earthquake. Due to its large wavelength and ability to penetrate, the SAR sensor can capture images at any time of day or night. However, the random and continuous interaction of high frequency electromagnetic radiations emitted from the SAR sensor with target areas causes constructive and destructive interference, which leads to speckle noise that makes the image less clear. It is hard to get information out of a situation like this. Not only do SAR images have speckle noise, but they also have geometric distortion, system nonlinear effects, and range migration, all of which need to be studied. Based on how they are used, SAR can be done in three different ways: Strip mapping is a type of SAR that is used to map large areas of terrain. Spotlight Mode SAR is used to capture images of a small area of terrain by looking at it from different angles. In inverse SAR, it is used to track the movement of a target. Deep learning methods like the convolutional neural network (CNN) make it possible to classify images and fix them in amazing ways. So, experts, academicians, researchers, and scientists need to come up with new SAR image processing methods and SAR raw signal modeling techniques to help them build new SAR systems. There is a total of ten papers published in this special section. The main goals of the section are to find the basic research questions about SAR image processing that are important for real-world SAR and other remote sensing applications that use deep learning techniques, track how well remote sensing problems are getting solved, and have experts, academicians, researchers, and scientists share their success stories of applying advanced deep learning techniques to real-world SAR and other remote sensing applications.