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
High-Resolution Remote Sensing Image Processing and Applications
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

High-Resolution Remote Sensing Image Processing and Applications

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

Deadline for manuscript submissions: 28 September 2025 | Viewed by 768

Special Issue Editors

Dr. Kai Xu

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: high resolution remote sensing image processing; night light remote sensing
Dr. Yixiang Chen

E-Mail Website
Guest Editor
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: high-resolution remote sensing; object detection; spatiotemporal prediction; Internet of Things
Prof. Dr. Kun Qin

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: spatio-temporal big data analysis and mining; intelligent processing of remote sensing images; spatial humanities and social geography

Special Issue Information

Dear Colleagues,

In the rapidly evolving field of remote sensing, high-resolution imagery is key in research and applications. Advanced sensors are now capable of capturing images with spatial and spectral detail, providing scientists and decision-makers with rich information to more accurately assess environmental changes and their impacts. High-resolution remote sensing imagery not only enhances the precision and efficiency of monitoring but also serves as a powerful tool for addressing global challenges. For instance, in environmental monitoring, it can precisely track deforestation, land use changes, and pollution sources; in precision agriculture, it helps farmers optimize crop management; and in urban planning, it provides data support for the development of smart cities. With continuous technological advancements, the application value of high-resolution remote sensing imagery in areas such as land use, environmental monitoring, precision agriculture, and human activity monitoring is becoming increasingly prominent. However, faced with vast amounts of remote sensing data, we also need to leverage AI technologies to address new challenges.

The core objective of this Special Issue is to unearth and share breakthrough achievements in high-resolution remote sensing image processing and its extensive application areas. We are dedicated to presenting novel algorithms that can break through the bottlenecks of traditional methods. We focus on exploring how high-resolution remote sensing can revolutionize different industries. By demonstrating these applications, we aim to inspire more researchers to apply high-resolution remote sensing technology in their respective fields and foster more effective solutions.

The scope of this Special Issue includes, but is not limited to, the following:

  • Advanced super-resolution techniques for high-resolution remote sensing images;
  • Remote sensing image registration and fusion;
  • Intelligent fusion methods for multi-source high-resolution images;
  • 3D reconstruction and visualization in high-resolution remote sensing;
  • Deep learning methods for high-resolution satellite image processing and interpretation;
  • Object detection and analysis for high-resolution remote sensing images;
  • Semantic segmentation for high-resolution remote sensing images;
  • Change detection for multi-temporal images;
  • Remote sensing vision-language models;
  • Artificial intelligence for unmanned aerial vehicle remote sensing;
  • High-resolution remote sensing for environmental monitoring;
  • High-resolution remote sensing in agriculture;
  • High-resolution remote sensing for urban planning;
  • High-resolution remote sensing for disaster management and emergency response.

Dr. Kai Xu
Dr. Yixiang Chen
Prof. Dr. Kun Qin
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

  • high-resolution remote sensing
  • deep learning
  • image processing
  • image fusion
  • object detection
  • semantic segmentation
  • change detection
  • remote sensing vision–language model.

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.

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

18 pages, 2930 KiB  
Article
Eye in the Sky for Sub-Tidal Seagrass Mapping: Leveraging Unsupervised Domain Adaptation with SegFormer for Multi-Source and Multi-Resolution Aerial Imagery
by Satish Pawar, Aris Thomasberger, Stefan Hein Bengtson, Malte Pedersen and Karen Timmermann
Remote Sens. 2025, 17(14), 2518; https://doi.org/10.3390/rs17142518 - 19 Jul 2025
Viewed by 213
Abstract
The accurate and large-scale mapping of seagrass meadows is essential, as these meadows form primary habitats for marine organisms and large sinks for blue carbon. Image data available for mapping these habitats are often scarce or are acquired through multiple surveys and instruments, [...] Read more.
The accurate and large-scale mapping of seagrass meadows is essential, as these meadows form primary habitats for marine organisms and large sinks for blue carbon. Image data available for mapping these habitats are often scarce or are acquired through multiple surveys and instruments, resulting in images of varying spatial and spectral characteristics. This study presents an unsupervised domain adaptation (UDA) strategy that combines histogram-matching with the transformer-based SegFormer model to address these challenges. Unoccupied aerial vehicle (UAV)-derived imagery (3-cm resolution) was used for training, while orthophotos from airplane surveys (12.5-cm resolution) served as the target domain. The method was evaluated across three Danish estuaries (Horsens Fjord, Skive Fjord, and Lovns Broad) using one-to-one, leave-one-out, and all-to-one histogram matching strategies. The highest performance was observed at Skive Fjord, achieving an F1-score/IoU = 0.52/0.48 for the leave-one-out test, corresponding to 68% of the benchmark model that was trained on both domains. These results demonstrate the potential of this lightweight UDA approach to generalization across spatial, temporal, and resolution domains, enabling the cost-effective and scalable mapping of submerged vegetation in data-scarce environments. This study also sheds light on contrast as a significant property of target domains that impacts image segmentation. Full article
(This article belongs to the Special Issue High-Resolution Remote Sensing Image Processing and Applications)
►▼ Show Figures

Figure 1

24 pages, 2440 KiB  
Article
A Novel Dynamic Context Branch Attention Network for Detecting Small Objects in Remote Sensing Images
by Huazhong Jin, Yizhuo Song, Ting Bai, Kaimin Sun and Yepei Chen
Remote Sens. 2025, 17(14), 2415; https://doi.org/10.3390/rs17142415 - 12 Jul 2025
Viewed by 222
Abstract
Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the [...] Read more.
Detecting small objects in remote sensing images is challenging due to their size, which results in limited distinctive features. This limitation necessitates the effective use of contextual information for accurate identification. Many existing methods often struggle because they do not dynamically adjust the contextual scope based on the specific characteristics of each target. To address this issue and improve the detection performance of small objects (typically defined as objects with a bounding box area of less than 1024 pixels), we propose a novel backbone network called the Dynamic Context Branch Attention Network (DCBANet). We present the Dynamic Context Scale-Aware (DCSA) Block, which utilizes a multi-branch architecture to generate features with diverse receptive fields. Within each branch, a Context Adaptive Selection Module (CASM) dynamically weights information, allowing the model to focus on the most relevant context. To further enhance performance, we introduce an Efficient Branch Attention (EBA) module that adaptively reweights the parallel branches, prioritizing the most discriminative ones. Finally, to ensure computational efficiency, we design a Dual-Gated Feedforward Network (DGFFN), a lightweight yet powerful replacement for standard FFNs. Extensive experiments conducted on four public remote sensing datasets demonstrate that the DCBANet achieves impressive mAP@0.5 scores of 80.79% on DOTA, 89.17% on NWPU VHR-10, 80.27% on SIMD, and a remarkable 42.4% mAP@0.5:0.95 on the specialized small object benchmark AI-TOD. These results surpass RetinaNet, YOLOF, FCOS, Faster R-CNN, Dynamic R-CNN, SKNet, and Cascade R-CNN, highlighting its effectiveness in detecting small objects in remote sensing images. However, there remains potential for further improvement in multi-scale and weak target detection. Future work will integrate local and global context to enhance multi-scale object detection performance. Full article
(This article belongs to the Special Issue High-Resolution Remote Sensing Image Processing and Applications)
►▼ Show Figures

Figure 1

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