Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Advances in Deep Learning Approaches in Remote Sensing (Second Edition)
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Deep Learning Approaches in Remote Sensing (Second 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: 31 August 2025 | Viewed by 1156

Special Issue Editors

Dr. Xiaobo Liu

E-Mail Website
Guest Editor
School of Automation, China Univerisity of Geosciences, Wuhan 430074, China
Interests: intelligent optimization; machine learning; hyperspectral image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yaoming Cai

E-Mail Website
Guest Editor
School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
Interests: machine learning and remote sensing image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of our previous Special Issue, “Advances in Deep Learning Approaches in Remote Sensing”, we are happy to announce that a new edition has been created.

Deep learning has witnessed an explosion of architectures of continuously growing capability and capacity. Benefiting from the rapid expansion of Earth observation data, deep learning has been effectively applied to various applications in remote sensing, including land use and land cover classification, scene classification, object detection, change detection, multimodal fusion, segmentation, and object-based image analysis. Nonetheless, as new challenges and opportunities emerge, the need for more advanced models, learning paradigms, and datasets to enable the efficient and effective processing and analysis of remote sensing data has become urgent.

This Special Issue aims to investigate the cutting-edge applications of deep learning in remote sensing. We invite research contributions and surveys in this area. Potential topics may include, but are not limited to, the following:

  • Deep learning techniques for feature extraction of remote sensing data;
  • Deep learning approaches for land cover and scene classification and clustering;
  • Multimodal deep learning and the fusion of multimodal remote sensing data;
  • Geometric deep learning for hyperspectral image processing;
  • Super-resolution reconstruction based on deep learning methods;
  • Change and object detection using deep learning methodologies;
  • Self-/un-/semi-/supervised methods for interpretation of remote sensing data;
  • Semantic segmentation of remote sensing images;
  • New remote sensing datasets.

Dr. Xiaobo Liu
Dr. Yaoming Cai
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

  • deep learning
  • remote sensing image processing
  • machine learning
  • multimodal fusion
  • representation learning
  • intelligent interpretation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

30 pages, 12920 KB  
Article
CSCN: A Cross-Scan Semantic Cluster Network with Scene Coupling Attention for Remote Sensing Segmentation
by Lei Zhang, Xing Xing, Changfeng Jing, Min Kong and Gaoran Xu
Remote Sens. 2025, 17(16), 2803; https://doi.org/10.3390/rs17162803 - 13 Aug 2025
Viewed by 357
Abstract
The spatial attention mechanism has been widely employed in the semantic segmentation of remote sensing images due to its exceptional capacity for modeling long-range dependencies. However, the analysis performance of remote sensing images can be reduced owing to their large intra-class variance and [...] Read more.
The spatial attention mechanism has been widely employed in the semantic segmentation of remote sensing images due to its exceptional capacity for modeling long-range dependencies. However, the analysis performance of remote sensing images can be reduced owing to their large intra-class variance and complex spatial structures. The vanilla spatial attention mechanism relies on the dense affine operations and a fixed scanning mechanism, which often introduces a large amount of redundant contextual semantic information and lacks consideration of cross-directional semantic connections. This paper proposes a new Cross-scan Semantic Cluster Network (CSCN) with integrated Semantic Filtering Contextual Cluster (SFCC) and Cross-scan Scene Coupling Attention (CSCA) modules to address these limitations. Specifically, the SFCC is designed to filter redundant information; feature tokens are clustered into semantically related regions, effectively identifying local features and reducing the impact of intra-class variance. CSCA effectively addresses the challenges of complex spatial geographic backgrounds by decomposing scene information into object distributions and global representations, using scene coupling and cross-scanning mechanisms and computing attention from different directions. Combining SFCC and CSCA, CSCN not only effectively segments various geographic spatial objects in complex scenes but also has low model complexity. The experimental results on three benchmark datasets demonstrate the outstanding performance of the attention model generated using this approach. Full article
(This article belongs to the Special Issue Advances in Deep Learning Approaches in Remote Sensing (Second Edition))
►▼ Show Figures

Figure 1

23 pages, 3410 KB  
Article
LinU-Mamba: Visual Mamba U-Net with Linear Attention to Predict Wildfire Spread
by Henintsoa S. Andrianarivony and Moulay A. Akhloufi
Remote Sens. 2025, 17(15), 2715; https://doi.org/10.3390/rs17152715 - 6 Aug 2025
Viewed by 520
Abstract
Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this [...] Read more.
Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource allocation, and decision making in disaster management. In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. We propose LinU-Mamba, a model with a U-Net-based vision Mamba architecture, with light spatial attention in skip connections, and an efficient linear attention mechanism in the encoder and decoder to better capture salient fire information in the dataset. The model is trained and evaluated on the two-dimensional remote sensing dataset Next Day Wildfire Spread (NDWS), which maps fire data across the United States with fire entries, topography, vegetation, weather, drought index, and population density variables. The results demonstrate that our approach achieves superior performance compared to existing deep learning methods applied to the same dataset, while showing an efficient training time. Furthermore, we highlight the impacts of pre-training and feature selection in remote sensing, as well as the impacts of linear attention use in our model. As far as we know, LinU-Mamba is the first model based on Mamba used for wildfire spread prediction, making it a strong foundation for future research. Full article
(This article belongs to the Special Issue Advances in Deep Learning Approaches in Remote Sensing (Second Edition))
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop