Synthetic Aperture Radar image analysis based on deep learning: A review of a decade of research

Artificial intelligence research in the area of computer vision teaches machines to comprehend and interpret visual data. Machines can properly recognize and classify items using digital images captured by cameras and videos, deep learning models, and then respond to what they observe. Similarly, artificial intelligence has also been able to learn complex images captured by Synthetic Aperture Radar (SAR) that are widely used for various purposes but still leave room for improvements. Researchers have proposed numerous approaches in this field, from SAR target detection to SAR target recognition. This paper presents a survey on the different techniques and architectures proposed in the literature for various SAR image applications. The paper covers a survey on target detection models and target recognition models and their respective workflow to analyze the techniques involved and the performances of these models. This paper makes novel discussions, comparisons, and observations. It highlights the advantages and disadvantages of different approaches to give researchers the idea of how each technique can influence the performance for adoption in the future. The potential future directions along with hybrid models on each processing method are also highlighted based on the study. •State-of-the-art techniques on SAR image processing comprising detection and recognition are discussed.•The different architectures and parameter settings of each technique are also examined.•The significant advantages and disadvantages of the existing techniques are also discussed to give researchers insights into each method.•Necessary observations on each processing technique are discussed based on the study.•Future approaches to improving the existing techniques for performance enhancement are also highlighted, along with a few potential models.