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Remote Sensing
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Super-Resolution and Reconstruction of Remote Sensing Images
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Super-Resolution and Reconstruction of Remote Sensing Images

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 2026 | Viewed by 473

Special Issue Editors

Dr. Xiaowen Liu

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Guest Editor
Test Center, National University of Defense Technology, Xi’an, China
Interests: inverse synthetic aperture radar; ship detection; radar imaging; inverse problem
Dr. Yijun Chen

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Guest Editor
National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China
Interests: convolutional neural network; deep reinforcement learning; synthetic aperture radar

Special Issue Information

Dear Colleagues,

Remote sensing images form well-established datasets for detection, classification, and monitoring of small-scale features critical in geospatial analysis applications such as land-cover classification, environmental monitoring, urban planning, and disaster assessment. The use of such images acquired by satellite sensors has confirmed their value in numerous remote sensing applications. Even though remote sensing images are widely used, they remain challenging due to the limitations of current imaging sensors and other factors like optical system aberrations, atmospheric disturbances, movement, noise of imaging systems, high data costs, etc. Meanwhile, the acquired low-resolution remote sensing images suffer from difficulties in many remote sensing applications such as map updating, road extraction, and military target identification. Other alternative or complementary technologies have been present in remote sensing image reconstruction in recent decades. The fast evolution of super-resolution techniques, breaking through the resolution limit of image acquisition equipment and realizing image reconstruction at sub-pixel levels with complementary low-resolution image sequence information, has popularized the use of super-resolution remote sensing images for spatial detail enhancement without requiring hardware upgrades for small-scale detail reconstruction.

This Special Issue aims to publish studies covering technology for the reconstruction of remote sensing images acquired by different super-resolution techniques. Topics may include several of the challenges in this area, including geometric misalignments, land-class imbalances, trade-offs between resolution and geographic coverage, among other issues. Different categories of techniques, such as learning-based, interpolation-based, frequency-domain-based, probability-based, etc., are welcome.

Dr. Xiaowen Liu
Dr. Yijun Chen
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 250 words) can be sent to the Editorial Office for assessment.

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

  • remote sensing
  • image super-resolution
  • deep learning
  • multiscale enhancement
  • image fusion
  • multiscale feature
  • frequency domain

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Published Papers (2 papers)

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Research

25 pages, 8816 KB  
Article
DFCFNet: A Local–Nonlocal Dual-Branch Feature Complementary Fusion Network for Remote Sensing Image Super-Resolution
by Miaomiao Zhang, Quan Wang, Wuxia Zhang, Xiangpeng Chen, Jiaxin Pan and Huinan Guo
Remote Sens. 2026, 18(10), 1626; https://doi.org/10.3390/rs18101626 - 19 May 2026
Abstract
Remote sensing image super-resolution (RSISR) has gained significant attention in recent years due to its critical role in enhancing image analysis capabilities. While existing methods often focus on nonlocal feature extraction, they frequently overlook the importance of local information integration. Moreover, many methods [...] Read more.
Remote sensing image super-resolution (RSISR) has gained significant attention in recent years due to its critical role in enhancing image analysis capabilities. While existing methods often focus on nonlocal feature extraction, they frequently overlook the importance of local information integration. Moreover, many methods reconstruct images by introducing more complex structures, which poses a challenge to resource-limited devices. To address these issues, we present a local–nonlocal dual-branch feature complementary fusion network (DFCFNet) featuring two key components: a lightweight dual-branch feature aggregation (DBFA) module and an Efficient Feed-Forward Network (EFFN). The DBFA employs a dual-branch structure comprising a Focused Local Feature Branch (FLFB) with novel Partial Convolution Channel Mixers for localized pattern modeling and a Non-Focal Exploration Branch (NFEB) utilizing global variance analysis for comprehensive feature extraction. This dual-branch design enables simultaneous capture of local and global contextual information. The EFFN is designed to further refine the features of the DBFA output in order to make full use of the detailed information of the image. Extensive experimental results show that the proposed DFCFNet reconstructs optimally on remote sensing datasets and is also optimal in terms of computational efficiency and network complexity. The framework’s versatility is further confirmed through successful adaptation to natural image SR tasks, showing consistent performance improvements across five standard datasets. Full article
(This article belongs to the Special Issue Super-Resolution and Reconstruction of Remote Sensing Images)
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25 pages, 19572 KB  
Article
KAN-Enhanced Alignment and Fusion for Lightweight Satellite Video Super-Resolution
by Junjie Xiong, Haopeng Zhang and Zhiguo Jiang
Remote Sens. 2026, 18(10), 1598; https://doi.org/10.3390/rs18101598 - 16 May 2026
Viewed by 75
Abstract
Satellite video super-resolution (SVSR) aims to reconstruct high-resolution video frames from low-resolution satellite observations, providing enhanced visual details for remote sensing applications. Despite recent progress, existing methods still suffer from limited alignment accuracy under complex motion and insufficient feature aggregation across frames, which [...] Read more.
Satellite video super-resolution (SVSR) aims to reconstruct high-resolution video frames from low-resolution satellite observations, providing enhanced visual details for remote sensing applications. Despite recent progress, existing methods still suffer from limited alignment accuracy under complex motion and insufficient feature aggregation across frames, which restricts reconstruction quality. To address these issues, we propose a lightweight SVSR framework that incorporates Kolmogorov–Arnold Networks (KAN) into both the alignment and fusion processes. Specifically, a KAN-based spatial attention module is introduced to enhance the first-order and second-order neighboring frames, improving the accuracy of frame alignment. In addition, a KAN-based channel attention mechanism is adopted to facilitate more effective multi-frame feature aggregation. Benefiting from these designs, the proposed framework achieves strong reconstruction capability while maintaining a lightweight model structure. Extensive experiments demonstrate that the proposed method achieves superior performance in terms of PSNR, SSIM, LPIPS, and tOF compared with existing approaches, verifying the effectiveness of integrating KAN into SVSR. Full article
(This article belongs to the Special Issue Super-Resolution and Reconstruction of Remote Sensing Images)
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