AI-Driven Remote Sensing Image Restoration and Generation

Dear Colleagues,

Advanced artificial techniques have made substantial contributions to intelligent remote sensing image interpretation. However, the availability of high-quality data for such data-intensive and model-heavy approaches is fundamentally limited by the high acquisition costs and degradation arising from atmospheric variability, sensor-induced distortions, and environmental factors. AI-driven remote sensing image restoration and generation aim to leverage advanced AI techniques to enhance data quality and usability, offering superior modeling capability and adaptability compared with traditional methods. Remote sensing image restoration focuses on recovering reliable information from degraded observations, ensuring accurate data for downstream analysis. Remote sensing image generation supplies diverse simulated data to support model training under increasingly stringent data requirements on the scale, diversity, and quality.

Recent progress in artificial intelligence, particularly multimodal foundation models and agent techniques, has become a central methodological paradigm in modern image processing. This Special Issue aims to explore recent advances, methodologies, and applications in AI-driven remote sensing image restoration and generation, covering novel theories, algorithms, and frameworks for improving image quality or synthesizing realistic remote sensing imagery, as well as emerging challenges and future opportunities.

Articles may address, but are not limited to, the following topics:

1. Advanced learning-based architectures for remote sensing image processing (e.g., multimodal foundation model, agent-based framework);
2. Remote sensing image and video restoration tasks (e.g., super-resolution, denoising, dehazing);
3. Remote sensing image and video generation frameworks (e.g., diffusion models, GAN, flow matching);
4. Multi-modal and cross-domain fusion techniques for remote sensing data (e.g., visible, hyperspectral, multispectral, SAR);
5. Others related applications for remote sensing imagery (e.g., evaluation metrics, benchmark, datasets).

Dr. Shi Chen
Dr. Jiaqi Ma
Dr. Jiang He
Dr. Kui Jiang
Dr. Xiangyong Cao
Prof. Dr. Yicong Zhou
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 250 words) can be sent to the Editorial Office for assessment.

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.