Explainable Artificial Intelligence (XAI) in Radar Imaging: Recent Advances and Future Directions

Dear Colleagues,

We invite you to contribute to a Special Issue on “Explainable Artificial Intelligence (XAI) in Radar Imaging: Recent Advances and Future Directions”. XAI is a rapidly growing field that seeks to make AI systems more transparent, interpretable, and understandable to human users. This is accomplished by either developing inherently explainable models or using techniques to generate explanations for existing models.

The significance of XAI is particularly apparent in military surveillance applications, where it provides accountability in AI system decision-making processes. In the realm of radar imaging, XAI can assist in understanding features and patterns in images used by the model, validate model decisions, identify and address biases or errors, and build trust in the results of the AI model by making its decision-making process more transparent.

This Special Issue is dedicated to the publication of original and high-quality research papers and comprehensive review articles that explore the latest advancements in the application of XAI to radar imaging. We are seeking submissions in related fields, including but not limited to:

• Automatic target recognition;
• Segmentation;
• Scene classification;
• Object detection and tracking;
• Change detection;
• Image denoising.

Additionally, we welcome contributions that integrate XAI with various cutting-edge technologies to enhance the functionality and performance of imaging radars. This includes the use of compact deep neural architectures, complex-valued structures, and techniques for open set recognition, incremental learning, and frugal learning. The contributions may also involve data augmentation techniques, transfer learning, knowledge distillation, generative models, active learning, reinforcement learning, regression models for estimating target parameters, physical model integration, and data fusion. The benchmark datasets may involve synthetic aperture radar (SAR), inverse SAR (ISAR), interferometric SAR (InSAR), polarimetric SAR (PolSAR) data, and so on.

We encourage authors to submit their original research articles or survey/review papers for publication in the open-access journal Remote Sensing. Please note that submissions should not currently be under review by other journals. Join us in advancing the field of XAI and shaping the future of AI in radar imaging. We look forward to your contribution!

Dr. Amir Hosein Oveis
Dr. Elisa Giusti
Dr. Selenia Ghio
Prof. Dr. Marco Martorella
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• explainable artificial intelligence (XAI)
• imaging radar
• synthetic aperture radar
• deep learning
• classification
• feature extraction
• change detection
• automatic target recognition (ATR)
• object detection
• convolutional neural networks