Deep Learning-Driven Cloud and Haze Mitigation: Advancements in Remote Sensing Image Restoration
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 15 January 2027 | Viewed by 895
Editors
Interests: remote sensing image enhancement; deep learning; optimization method for image restoration
Interests: satellite; satellite image processing; geospatial science; image matching
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing satellite systems have evolved into sophisticated, multidimensional platforms that can increasingly provide various observation and temporal–spatial–spectrum coverage information. Rich geospatial information can be extracted from optical remote sensing images (RSIs), driving significant progress across Earth observation, environment monitoring, disaster emergency response, resource exploration, and so on.
However, owing to the absorption and scattering effects caused by haze, thin clouds, and thick clouds, ultimately, RSIs have to be degraded and even suffer from information loss. Having to face the practical problems of cloud and haze, cloud/haze removal approaches based on single RSIs, multi-temporal RSIs, and multi-modal RSIs have developed rapidly in recent decades. Many researchers have devoted themselves to restoring clean RSIs by proposing model-driven and data-driven algorithms. Regarding the latter, the advent of deep learning has precipitated a paradigm shift, endowing the field with remarkable capabilities in feature representation, mining semantic priors, and contextual information, which enable the achievement of high precision and highly reliable RSI cloud removal.
This Special Issue aims to assemble pioneering and innovative studies covering data learning-driven cloud and haze removal in the field of remote sensing. Topics may cover anything from cloud and haze mitigation with advanced deep learning architectures and novel algorithms, including CNNs, Transformers, Generative models, Diffusion models, Mamba, and the mixed. Similarly, novel cloud and haze mitigation involving multi-source data, such as fusion of multi-temporal optical data or optical-SAR data, is also welcome.
The potential themes include, but are not limited to, the following:
- Novel deep learning architectures for cloud/haze removal.
- Physics-guided and interpretable deep learning methods.
- Multi-modal and multi-source fusion.
- Transfer learning and domain adaptation.
- Restoration quality metrics and evaluation.
- Downstream remote sensing applications.
Dr. Jie Han
Dr. Zhen Ye
Prof. Dr. Huifang Li
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-anonymized 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.
Keywords
- deep learning
- dehazing
- cloud removal
- image restoration
- multi-modal fusion
- restoration quality
- remote sensing
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