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Electronics
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New Challenges in Remote Sensing Image Processing
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New Challenges in Remote Sensing Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 2748

Special Issue Editors

Dr. Kaiqiang Chen

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: remote sensing; feature maps; object detection; remote sensing images; bounding box; convolutional neural network; building extraction
Prof. Dr. Yu Weng

E-Mail Website
Guest Editor
Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing 100081, China
Interests: big data analysis; artificial intelligence and deep learning; intelligent protection of ancient books; multimodal learning
Special Issues, Collections and Topics in MDPI journals
Dr. Zide Fan

E-Mail Website
Guest Editor
Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: computer vision; remote sensing image understanding; geospatial data mining and visualization
Dr. Zicong Zhu

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
Interests: computer vision; pattern recognition; remote sensing image interpretation; especially on object detection; image pretraining; cross-modal image–text retrieval

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to our Special Issue on “New Challenges in Remote Sensing Image Processing”. This rapidly evolving field is of great importance, especially with the increasing availability of high-resolution remote sensing data. There is a pressing need for innovative image processing techniques to extract meaningful information efficiently and accurately. This Special Issue aims to highlight the latest advancements and ongoing challenges in remote sensing image processing. It is in line with the scope of Electronics of fostering cutting-edge research in image processing and related areas. We aim to have a collection of at least 10 articles, and this Special Issue may be printed in book form if this number is reached. In this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, machine learning and artificial intelligence applications, multimodal data fusion, image enhancement and restoration, SAR imaging and processing, change detection, object detection and classification, 3D reconstruction, and the handling of big data analysis and real-time processing challenges.

We look forward to receiving your contributions.

Technical Program Committee Members:

  1. Xiyu Qi Aerospace Information Research Institute, CAS
  2. Yi Wang Aerospace Information Research Institute, CAS
  3. Zheng Liu Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance, Ministry of Education
  4. Yidan Zhang Aerospace Information Research Institute, Chinese Academy of Sciences

Dr. Kaiqiang Chen
Prof. Dr. Yu Weng
Dr. Zide Fan
Dr. Zicong Zhu
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. Electronics 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 2400 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
  • machine learning
  • multimodal data fusion
  • image enhancement
  • SAR imaging and processing
  • change detection
  • object detection
  • 3D reconstruction
  • big data analysis
  • real-time processing

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

Published Papers (4 papers)

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Research

22 pages, 121478 KiB  
Article
Ground-Moving Target Relocation for a Lightweight Unmanned Aerial Vehicle-Borne Radar System Based on Doppler Beam Sharpening Image Registration
by Wencheng Liu, Zhen Chen, Zhiyu Jiang, Yanlei Li, Yunlong Liu, Xiangxi Bu and Xingdong Liang
Electronics 2025, 14(9), 1760; https://doi.org/10.3390/electronics14091760 - 25 Apr 2025
Viewed by 71
Abstract
With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a [...] Read more.
With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground-moving target indication (GMTI) radar systems has received great interest. However, because of size, weight, and power (SWaP) limitations, the UAV may not be able to equip a highly accurate inertial navigation system (INS), which leads to reduced accuracy in the moving target relocation. To solve this issue, we propose using an image registration algorithm, which matches a Doppler beam sharpening (DBS) image of detected moving targets to a synthetic aperture radar (SAR) image containing coordinate information. However, when using conventional SAR image registration algorithms such as the SAR scale-invariant feature transform (SIFT) algorithm, additional difficulties arise. To overcome these difficulties, we developed a new image-matching algorithm, which first estimates the errors of the UAV platform to compensate for geometric distortions in the DBS image. In addition, to showcase the relocation improvement achieved with the new algorithm, we compared it with the affine transformation and second-order polynomial algorithms. The findings of simulated and real-world experiments demonstrate that our proposed image transformation method offers better moving target relocation results under low-accuracy INS conditions. Full article
(This article belongs to the Special Issue New Challenges in Remote Sensing Image Processing)
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19 pages, 9660 KiB  
Article
An Efficient Synthetic Aperture Radar Interference Suppression Method Based on Image Domain Regularization
by Xuyang Ge, Xingdong Liang, Hang Li, Zhiyu Jiang, Yuan Zhang and Xiangxi Bu
Electronics 2025, 14(5), 1054; https://doi.org/10.3390/electronics14051054 - 6 Mar 2025
Viewed by 729
Abstract
Synthetic aperture radar (SAR) systems, as wideband radar systems, are inherently susceptible to interference signals within their operational frequency band, which significantly affects SAR signal processing and image interpretation. Recent studies have demonstrated that semiparametric methods (e.g., the RPCA method) exhibit excellent performance [...] Read more.
Synthetic aperture radar (SAR) systems, as wideband radar systems, are inherently susceptible to interference signals within their operational frequency band, which significantly affects SAR signal processing and image interpretation. Recent studies have demonstrated that semiparametric methods (e.g., the RPCA method) exhibit excellent performance in suppressing these interference signals. However, these methods predominantly focus on processing SAR’s raw echo data, which does not satisfy the sparsity requirements and entails extremely high computational complexity, complicating integration with imaging algorithms. This paper introduces an effective method for suppressing interference signals by leveraging the sparsity of the SAR image domain. It utilizes the sparsity of the interference signal in the two-dimensional frequency domain, following focusing processing, rather than relying on low-rank properties. This approach significantly reduces the computational complexity. Ultimately, the effectiveness and efficiency of the proposed algorithm are validated through experiments conducted with simulated and real SAR data. Full article
(This article belongs to the Special Issue New Challenges in Remote Sensing Image Processing)
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19 pages, 35731 KiB  
Article
Robust Synchronization Error Estimation Under Multipath Fading in Distributed SAR
by Jihao Xin, Xingdong Liang, Zhiyu Jiang, Hang Li, Yujie Dai, Huan Wang, Yuan Zhang and Xiangxi Bu
Electronics 2025, 14(5), 983; https://doi.org/10.3390/electronics14050983 - 28 Feb 2025
Viewed by 499
Abstract
Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath [...] Read more.
Unmanned Aerial Vehicle (UAV)-based distributed Synthetic Aperture Radar (SAR) is a current research focus. Phase synchronization is crucial for eliminating the non-coherence of distributed systems. However, as the number of UAVs increases, fast time-varying multipath effects caused by rotors can lead to multipath fading. This degrades the signal-to-noise ratio (SNR) of the synchronization link and distorts the synchronization waveform. It further breaks the reciprocity of the dual one-way synchronization link, ultimately degrading phase synchronization accuracy. We propose a robust method for spike detection and error propagation to improve phase synchronization precision. Using the Hampel filter, we detect pulse peak position jitter and remove observations from anomalous links. We then use data fusion based on minimum variance to recover synchronization errors in these links, leveraging the redundancy in synchronization phase matrices. The effectiveness of the proposed method is confirmed through flight test data from a four-UAV distributed TomoSAR experiment. Compared to the maximum-peak detection method, the phase accuracy is improved from 12.84 deg to 0.61 deg. This method supports the application of distributed SAR. Full article
(This article belongs to the Special Issue New Challenges in Remote Sensing Image Processing)
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24 pages, 6656 KiB  
Article
Large-Scale Stitching of Hyperspectral Remote Sensing Images Obtained from Spectral Scanning Spectrometers Mounted on Unmanned Aerial Vehicles
by Hong Liu, Bingliang Hu, Xingsong Hou, Tao Yu, Zhoufeng Zhang, Xiao Liu, Xueji Wang and Zhengxuan Tan
Electronics 2025, 14(3), 454; https://doi.org/10.3390/electronics14030454 - 23 Jan 2025
Viewed by 857
Abstract
To achieve large-scale stitching of the hyperspectral remote sensing images obtained by unmanned aerial vehicles (UAVs) equipped with an acousto-optic tunable filter spectrometer, this study proposes a method based on a feature fusion strategy and a seam-finding strategy using hyperspectral image classification. In [...] Read more.
To achieve large-scale stitching of the hyperspectral remote sensing images obtained by unmanned aerial vehicles (UAVs) equipped with an acousto-optic tunable filter spectrometer, this study proposes a method based on a feature fusion strategy and a seam-finding strategy using hyperspectral image classification. In the feature extraction stage, SuperPoint deep features from images in different spectral segments of the data cube were extracted and fused. The feature depth matcher, LightGlue, was employed for feature matching. During the data cube fusion stage, unsupervised K-means spectral classification was performed separately on the two hyperspectral data cubes. Subsequently, grayscale transformations were applied to the classified images. A dynamic programming method, based on a grayscale loss function, was then used to identify seams in the transformed images. Finally, the identified splicing seam was applied across all bands to produce a unified hyperspectral data cube. The proposed method was applied to hyperspectral data cubes acquired at specific waypoints by UAVs using an acousto-optic tunable filter spectral imager. Experimental results demonstrated that the proposed method outperformed both single-spectral-segment feature extraction methods and stitching methods that rely on seam identification from a single spectral segment. The improvement was evident in both the spatial and spectral dimensions. Full article
(This article belongs to the Special Issue New Challenges in Remote Sensing Image Processing)
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