Computer Vision, Neural Networks and Deep Learning for SAR Image Processing

Dear Colleagues,

Synthetic Aperture Radar (SAR) is a widely used remote sensing technology capable of capturing high-resolution images under all weather conditions and during both day and night. However, SAR image processing presents unique challenges due to speckle noise, geometric distortions, and complex scattering mechanisms. Traditional methods often struggle with these complexities, prompting the need for advanced techniques. Recent advances in computer vision, neural networks (NNs), and deep learning (DL) have revolutionized SAR image interpretation. Convolutional Neural Networks (CNNs), Transformers, Generative Adversarial Networks (GANs), and other DL architectures have shown remarkable success in tasks of SAR data processing. This Special Issue seeks high-quality research contributions that leverage computer vision, NN, and DL techniques to address SAR image processing challenges. Potential topics include (but are not limited to) the following: SAR image classification and segmentation, object detection, change detection, parameter inversion, multi-modal fusion with SAR/optical/LiDAR data, agricultural remote sensing, forest application, soil moisture inversion, geohazard monitoring, and disaster response. This Special Issue aims to compile state-of-the-art methodologies, providing a reference for future developments in AI-driven SAR image processing.

Prof. Dr. Changcheng Wang
Dr. Peng Shen
Prof. Dr. Alin Achim
Guest Editors

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