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Remote Sensing
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Remote Sensing Data Fusion and Applications (2nd Edition)
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Remote Sensing Data Fusion and Applications (2nd Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 728

Special Issue Editors

Dr. Linwei Yue

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: remote sensing image processing and applications; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Qing Cheng

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing information processing and applications; quality improvement of remote sensing images; data fusion; regional and global environmental changes
Special Issues, Collections and Topics in MDPI journals
Dr. Xinghua Li

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: image processing; multitemporal remote sensing; change detection; cloud removal; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jiang He

E-Mail
Co-Guest Editor
Chair of Data Science in Earth Observation, Technical University of Munich, 81675 Munich, Germany
Interests: model&data-driven; AI4EO; earth observation; data processing; deep learning

Special Issue Information

Dear Colleagues,

The use of remote sensing technology is widespread in our world because it is one of the most effective methods with which to observe the earth. There are a variety of remote sensing platforms, e.g., ground, aerial, and space ones, that carry optical, infrared, radar, and lidar sensors. The processing methods and practical applications of data obtained from remote sensing have received increasing interest from the remote sensing community. The abundance of remote sensing data also presents new opportunities and challenges for researchers. Remote sensing data fusion from multiple sensors has greatly benefited many applications that require more extensive temporal, spatial, or spectral information than any individual sensor can provide.

This Special Issue derives its title from the 9th Youth Geosciences Forum, held on 5–8 May 2025 (http://www.qndxlt.com/index.html). This Special Issue aims to present the most recent research and developments on remote sensing data fusion from sensors at different spatial and temporal resolutions. Topics include, but are not limited to, novel image fusion algorithms based on transform domain, machine learning, and other theoretical approaches. Applications in earth observation are also welcome.

Dr. Linwei Yue
Prof. Dr. Qing Cheng
Dr. Xinghua Li
Dr. Jiang He
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.

Keywords

  • spatio-temporal data fusion
  • multimodal data fusion
  • multitemporal data fusion
  • multi-sensor data fusion
  • deep learning for data fusion
  • image enhancement and restoration

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Published Papers (1 paper)

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Research

22 pages, 14292 KiB  
Article
A Feature-Reinforced Ensemble Learning Framework for Space-Based DEM Correction
by Zidu Ouyang, Cui Zhou, Di Zhang, Zhiwei Liu, Jianjun Zhu and Jian Xie
Remote Sens. 2025, 17(8), 1337; https://doi.org/10.3390/rs17081337 - 9 Apr 2025
Viewed by 314
Abstract
Near-global Digital Elevation Model (DEM) products generated through space-based radar techniques have become a basic data source for a variety range of applications. However, these DEM products often contain typical errors such as vegetation bias and topography-related errors, which impede their practical utility. [...] Read more.
Near-global Digital Elevation Model (DEM) products generated through space-based radar techniques have become a basic data source for a variety range of applications. However, these DEM products often contain typical errors such as vegetation bias and topography-related errors, which impede their practical utility. Despite the development of numerous correction methods based on mathematical fitting and artificial neural networks over recent decades, reliably correcting large-scale spaceborne radar-derived DEMs remains an open challenge due to issues like underfitting or overfitting. This paper introduces a novel framework called Feature-Reinforced Ensemble Learning (FREEL) designed specifically for correcting space-based radar-derived DEMs. Within this FREEL framework, a feature derivation module and a feature reinforcement module are integrated to enhance the original input features. Subsequently, an adaptive weighting variant of the DeepForest algorithm is proposed to emphasize critical features and improve training robustness, even with limited training data. The Shuttle Radar Topographic Mission (SRTM) DEMs of Hunan Province, China, characterized by diverse surface terrain and vegetation coverage, were selected to evaluate the FREEL framework. The results indicate that the accuracy of the SRTM DEM corrected using the FREEL framework improved by 40%, surpassing several mathematical fitting and machine learning baseline algorithms by an average of 45% and 23%, respectively. This method provides a more robust solution for correcting near-global space-based radar-derived DEM products. Full article
(This article belongs to the Special Issue Remote Sensing Data Fusion and Applications (2nd Edition))
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