Applications and Analysis of Satellite Cloud Imagery Using Deep Learning Techniques
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 5924
Special Issue Editors
Interests: aviation meteorology; aviation safety; mountain meteorology; meteorological instrumentation
Special Issues, Collections and Topics in MDPI journals
Interests: atmospheric observations; wind engineering; structural health monitoring; computational fluid mechanics; structural engineering
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; image recognition; graph neural network and multimedia content analysis
Special Issues, Collections and Topics in MDPI journals
Interests: wind engineering; bridge engineering; structural engineering; hurricane resilience; machine learning; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: atmospheric physics; climate change; remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the improvements made in satellite remote sensing technology and imaging technology, the spatial resolution and timeliness of satellite cloud image data have been dramatically improved. These data provide potent means for monitoring disastrous weather, such as typhoons and rainstorms, and play a vital role in weather forecasts and short-term climate prediction. However, owing to the increasing complexity of satellite cloud images in both temporal and spatial dimensions, traditional methods cannot effectively recognize and analyze these data. Over the past few years, deep learning techniques, such as convolution neural network, recurrent neural network and recent vision transformer, have achieved great success in various computer vision applications by automatically capturing and learning the key features of image data. Their powerful feature extraction abilities show great potential for analyzing complex spatio-temporal data like satellite cloud images.
This Special Issue invites scholars to submit manuscripts that present new deep learning models or introduce the most advanced deep learning techniques for processing and analyzing satellite cloud images. As this is a broad area, there are no constraints regarding the field of applications. Potential topics include, but discussions are not limited to, the following areas:
- Satellite cloud image classification;
- Satellite cloud image restoration;
- Satellite cloud image prediction;
- Object detection of satellite cloud image;
- Spatio-temporal analysis of satellite cloud image;
- Applications to satellite cloud image
Dr. Pak-Wai Chan
Dr. Yun-Cheng He
Dr. Yang-Tao Wang
Dr. Teng Wu
Dr. Ismail Gultepe
Guest Editors
Manuscript Submission Information
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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.
Keywords
- satellite cloud image
- deep learning
- object detection
- classification
- prediction
- restoration
- tropical cyclone
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