Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.4
4.3
Journals
Remote Sensing
Special Issues
Artificial Intelligence for Optical Remote Sensing Image Processing
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

Artificial Intelligence for Optical Remote Sensing Image Processing

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "AI Remote Sensing".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 3578

Editors

Prof. Dr. Wei Fang

E-Mail Website
Guest Editor
School of Computer, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: artificial intelligence applications in atmospheric science; artificial intelligence applications in severe weather prediction; artificial intelligence applications in climate change; convective weather; data mining and knowledge discovery; remote sensing image processing; applied meteorology; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Daniele Bortoli

E-Mail Website
Guest Editor
Institute of Earth Sciences (ICT), University of Évora, 7000-803 Évora, Portugal
Interests: atmospheric trace gases; DOAS techniques; aerosol remote sensing; ozone and UV/Vis spectroscopy; ACTRIS and EARLINET networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of remote sensing, optical remote sensing image processing plays a pivotal role in extracting valuable information from the Earth's surface. With the rapid advancement of technology, artificial intelligence (AI) has emerged as a game-changer in this field, revolutionizing the way we analyze and interpret optical remote sensing images. This field integrates machine learning, deep learning and computer vision techniques to address challenges such as noise reduction, feature extraction and semantic segmentation in satellite and aerial imagery. Innovations like convolutional neural networks (CNNs) and generative adversarial networks (GANs) enable real-time analysis, supporting applications in environmental monitoring, urban planning and disaster response. By leveraging AI, researchers and practitioners can unlock deeper insights from remote sensing data, paving the way for smarter decision-making and sustainable development.

Moreover, AI can also be used for image restoration and enhancement. Remote sensing images often suffer from various distortions, such as noise, blurring and atmospheric interference. AI algorithms can effectively remove these distortions, improve the image quality and make it easier to extract meaningful information. In addition, AI enables the integration of remote sensing data with other data sources, such as geographic information system (GIS) and meteorological data. This integrated approach provides a more comprehensive understanding of the Earth's environment and helps in making more informed decisions in fields like agriculture, environmental monitoring and disaster management.

In conclusion, AI has brought about a new era in optical remote sensing image processing. It has not only improved the efficiency and accuracy of image analysis, but also opened up new possibilities for various applications. As AI technology continues to evolve, we can expect even more innovative solutions in this field, further advancing our ability to monitor and understand our planet.

Prof. Dr. Wei Fang
Prof. Dr. Daniele Bortoli
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-anonymized 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

  • artificial intelligence for science
  • optical remote sensing
  • image processing
  • geographic information system
  • noise reduction and image enhancement
  • feature extraction and semantic segmentation
  • super-resolution reconstruction
  • satellite imagery
  • computer vision
  • multimodal data integration
  • heatwave warning
  • disaster management
  • crop monitoring
  • environmental monitoring
  • urban planning
  • infrastructure inspection

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

38 pages, 16872 KB  
Article
SpecGateNet: Spectral-Guided Fusion Network for Cloud and Cloud Shadow Segmentation in Optical Remote Sensing Imagery
by Kaibo Qin, Shengyan Liu, Wengzheng Wu, Haoyu Yin, Tengyue Guo and Min Xia
Remote Sens. 2026, 18(15), 2469; https://doi.org/10.3390/rs18152469 - 28 Jul 2026
Viewed by 449
Abstract
Accurate segmentation of clouds and cloud shadows in optical remote sensing imagery is an indispensable preprocessing step for downstream land-surface observation tasks. However, existing methods strike different trade-offs between local detail preservation and global context modeling, yet they still exhibit two major limitations: [...] Read more.
Accurate segmentation of clouds and cloud shadows in optical remote sensing imagery is an indispensable preprocessing step for downstream land-surface observation tasks. However, existing methods strike different trade-offs between local detail preservation and global context modeling, yet they still exhibit two major limitations: insufficient interaction during the dual-branch encoding stage, and decoder-side multi-scale fusion that applies uniform operations to all spatial locations, making it unable to distinguish boundary regions, such as thin cloud boundaries and cloud shadow contours, from homogeneous regions, such as thick cloud interiors. To address these issues, we propose SpecGateNet, a fusion network guided by spectral information from a frequency-domain perspective. The core idea is that the Fourier amplitude spectrum of deep features summarizes how much energy is carried by different spatial frequencies, where high-frequency components typically correspond to rapidly varying regions such as boundaries and textures, whereas low-frequency components are more closely associated with smooth regions. Building on this insight, we use an amplitude-derived, phase-free spectral energy cue constructed from the amplitude spectrum, and integrate it with a learnable gating generation network to produce spatially adaptive fusion modulation signals. SpecGateNet is built upon a CNN-Swin Transformer dual-branch encoder and consists of three key components: a Cross-Feature Fusion module (CFF) that enables stage-wise bidirectional interaction, a frequency-domain dynamic filtering bottleneck that enhances global context through input-adaptive frequency-domain filtering, and a Spectral-Guided Fusion decoder (SGF) that generates spatial gates from the amplitude spectrum to adaptively balance high-level semantics and low-level details. Together, the bottleneck filter and SGF constitute a spectral utilization framework that operates at both the encoder and decoder sides. Using only RGB inputs without relying on infrared auxiliary bands, SpecGateNet achieves the highest mIoU among the compared general-purpose baselines under a unified experimental protocol on three public datasets, namely CloudSEN-12, SPARCS-Val, and 38-Cloud, with mIoU scores of 77.80%, 76.75%, and 93.30%, respectively, outperforming the second-best method by 1.33, 5.26, and 1.44 percentage points. Ablation studies confirm that the spectral modules account for 70.1% of the total performance gain, with SGF contributing the largest single-step improvement among individual modules (+1.22%). Full article
(This article belongs to the Special Issue Artificial Intelligence for Optical Remote Sensing Image Processing)
►▼ Show Figures

Figure 1

25 pages, 2896 KB  
Article
A Multi-Scale Windowed Spatial and Channel Attention Network for High-Fidelity Remote Sensing Image Super-Resolution
by Xiao Xiao, Xufeng Xiang, Jianqiang Wang, Liwen Wang, Xingzhi Gao, Yang Chen, Jun Liu, Peng He, Junhui Han and Zhiqiang Li
Remote Sens. 2025, 17(21), 3653; https://doi.org/10.3390/rs17213653 - 6 Nov 2025
Viewed by 1646
Abstract
Remote sensing image super-resolution (SR) plays a crucial role in enhancing the quality and resolution of satellite and aerial imagery, which is essential for various applications, including environmental monitoring and urban planning. While recent image super-resolution networks have achieved strong results, remote-sensing images [...] Read more.
Remote sensing image super-resolution (SR) plays a crucial role in enhancing the quality and resolution of satellite and aerial imagery, which is essential for various applications, including environmental monitoring and urban planning. While recent image super-resolution networks have achieved strong results, remote-sensing images present domain-specific challenges—complex spatial distribution, large cross-scale variations, and dynamic topographic effects—that can destabilize multi-scale fusion and limit the direct applicability of generic SR models. These features make it difficult for single-scale feature extraction methods to fully capture the complex structure, leading to the presence of artifacts and structural distortion in the reconstructed remote sensing images. Therefore, new methods are needed to overcome these challenges and improve the accuracy and detail fidelity of remote sensing image super-resolution reconstruction. This paper proposes a novel Multi-scale Windowed Spatial and Channel Attention Network (MSWSCAN) for high-fidelity remote sensing image super-resolution. The proposed method combines multi-scale feature extraction, window-based spatial attention, and channel attention mechanisms to effectively capture both global and local image features while addressing the challenges of fine details and structural distortion. The network is evaluated on several benchmark datasets, including WHU-RS19, UCMerced and RSSCN7, where it demonstrates superior performance in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to state-of-the-art methods. The results show that the MSWSCAN not only enhances texture details and edge sharpness but also reduces reconstruction artifacts. To address cross-scale variations and dynamic topographic effects that cause texture drift in multi-scale SR, we combine windowed spatial attention to preserve local geometry with a channel-aware fusion layer (FFL) that reweights multi-scale channels. This stabilizes cross-scale aggregation at a runtime comparable to DAT and yields sharper details on heterogeneous land covers. Averaged over WHU–RS19, RSSCN7, and UCMerced_LandUse at ×2/×3/×4, MSWSCAN improves PSNR (peak signal-to-noise ratio, dB)/SSIM (structural similarity index measure, 0–1) by +0.10 dB/+0.0038 over SwinIR and by +0.05 dB/+0.0017 over DAT. In conclusion, the proposed MSWSCAN achieves state-of-the-art performance in remote sensing image SR, offering a promising solution for high-quality image enhancement in remote sensing applications. Full article
(This article belongs to the Special Issue Artificial Intelligence for Optical Remote Sensing Image Processing)
►▼ 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