Deep Learning-Based Cloud Detection and Removal for Remote Sensing Images

Dear Colleagues,

In today’s world, remote sensing images have emerged as indispensable data resources across diverse domains, including urban development planning, environmental surveillance, and agricultural evaluation. Cloud detection and removal in remote sensing imagery, which are fundamental to remote sensing data processing, hold extensive applications and far-reaching implications in multiple sectors.

Urban Development Planning: In the context of urban planning, cloud-free remote sensing images play a crucial role in tracking urban expansion and assessing infrastructure. By eliminating cloud cover from remote sensing data, urban planners can gain a clear and comprehensive view of the layout and evolution of urban buildings, transportation networks, and green spaces. This visual clarity facilitates the formulation of more rational urban development blueprints, the optimization of urban spatial configurations, and the enhancement of urban living conditions, thereby promoting the long-term, healthy development of cities.

Environmental Surveillance: Unobstructed by clouds, remote sensing images are pivotal for achieving a precise and accurate understanding of the Earth's surface environment. For instance, during the continuous monitoring of forest cover dynamics, the presence of clouds in remote sensing images can lead to misjudgments regarding the actual forest acreage. Employing advanced cloud detection and removal algorithms, we can ensure that the data obtained faithfully represent the real-time state of the forest ecosystem. This, in turn, enables the timely detection of deforestation, forest degradation, and other environmental changes, providing a solid foundation for effective environmental protection strategies.

Agricultural Evaluation: Cloud-free remote sensing images are of great value in monitoring crop growth and predicting yields. High-resolution remote sensing data, free from cloud interference, can offer detailed insights into various aspects of crop development, such as growth stages, nutrient deficiencies, and pest infestations. Armed with this information, farmers and agricultural researchers can make well-informed decisions regarding fertilization, irrigation, and pest management. As a result, not only can crop yields be enhanced, but the overall quality of agricultural products can also be improved, contributing to sustainable agricultural development.

In summary, the application of cloud detection and removal techniques in remote sensing images holds vast potential and is of great significance for driving progress in multiple fields. To further advance this field, we intend to compile a Special Issue on remote sensing image cloud detection and removal. We sincerely invite experts and scholars from around the world to contribute via sharing their latest research findings and cutting-edge developments in this area.

We welcome the submission of papers related to the following topics:

• Algorithms for remote sensing image cloud detection;
• Techniques for remote sensing image cloud removal;
• Deep-learning-based approaches for cloud detection and removal;
• Cloud processing in multispectral and hyperspectral remote sensing images;
• Spatio-temporal analysis of cloud-contaminated remote sensing data;
• The fusion of cloud-free remote sensing data with other data sources (e.g., LiDAR, ground-based observations);
• Cloud-free remote sensing applications in emerging interdisciplinary fields such as climate-smart agriculture and urban ecological planning;
• The development and application of real-time cloud detection and removal systems for dynamic remote sensing monitoring scenarios.

Dr. Jun Li
Dr. Libo Wang
Dr. Wufan Zhao
Guest Editors

Dr. Yang Du
Guest Editor Assistant

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.

• remote sensing
• cloud detection
• cloud removal
• image restoration
• data fusion
• pattern recognition