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Remote Sensing
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Temporal and Spatial Analysis of Multi-Source Remote Sensing Images
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Temporal and Spatial Analysis of Multi-Source 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 May 2025 | Viewed by 2300

Special Issue Editors

Prof. Dr. Lin Lei

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: deep learning; change detection; heterogenous images
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiyong Lv

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Xi’An University of Technology, Jin Hua South Road No.5, Xi'An 710054, China
Interests: very high-resolution remote sensing images; land cover change detection; landslide inventory mapping; land cover classification and pattern recognition; remote sensing application; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yuli Sun

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410000, China
Interests: remote sensing; pattern recognition; image processing; target detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the rapid advancement of remote sensing technologies has significantly enhanced our ability to monitor and analyze Earth's dynamic processes. The integration of multi-source remote sensing images—acquired from various sensors, platforms, and modalities—has opened new avenues for temporal and spatial analysis, providing richer and more comprehensive insights into environmental changes.

This Special Issue aims to explore the latest methodologies, applications, and challenges associated with the temporal and spatial analysis of multi-source remote sensing images. Topics of interest include, but are not limited to, the following:

Data Fusion Techniques: novel methods for integrating data from different sensors and platforms to enhance spatial and temporal resolution.

Change Detection Algorithms: advanced algorithms designed to identify and quantify changes in land cover, vegetation, urbanization, and other phenomena across multiple timeframes.

Image Registration Methods: new strategies for accurately registering multi-source remote sensing images, including advancements in algorithms that accommodate varying imaging conditions and sensor characteristics.

Multi-source Image Classification Approaches: innovative techniques for classifying land cover types within multi-source images, focusing on the integration of spectral, spatial, and temporal information to improve accuracy and robustness.

Case Studies and Applications: real-world applications demonstrating the effectiveness of multi-source remote sensing in fields such as disaster management, agriculture, urban planning, and climate change research.

Prof. Dr. Lin Lei
Dr. ZhiYong Lv
Dr. Yuli Sun
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

  • remote sensing images
  • multi-source data
  • spatial-temporal analysis
  • machine learning and deep learning
  • multi-modal image fusion
  • change detection
  • feature alignment

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Published Papers (4 papers)

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Research

23 pages, 21915 KiB  
Article
Spatiotemporal Dynamics of Habitat Quality in Semi-Arid Regions: A Case Study of the West Songnen Plain, China
by Hao Yu, Zhimin Liang, Rong Zhang, Mingming Jia, Shicheng Li, Xiaoyan Li and Huiying Li
Remote Sens. 2025, 17(10), 1663; https://doi.org/10.3390/rs17101663 - 8 May 2025
Viewed by 301
Abstract
Maintaining or improving habitat quality is essential for conserving biodiversity and ensuring the long-term survival of species. Nevertheless, increasing global warming and intensifying human activities have led to varying degrees of habitat degradation and biodiversity loss, especially in semi-arid regions. Focusing on China’s [...] Read more.
Maintaining or improving habitat quality is essential for conserving biodiversity and ensuring the long-term survival of species. Nevertheless, increasing global warming and intensifying human activities have led to varying degrees of habitat degradation and biodiversity loss, especially in semi-arid regions. Focusing on China’s West Songnen Plain—the nation’s largest saline-alkali region confronting acute environmental challenges—this study introduced the soil salinization level and mean NDVI of farmland during the growing season as dynamic threat factors and systematically explored the spatiotemporal dynamic characteristics of habitat quality in the semiarid area of the West Songnen Plain from 1990 to 2020. The results showed the following: (1) Habitat quality exhibited a continuous decline during the study period, following a “degradation–recovery” trajectory with deterioration peaking in 2010; the low- and poor-quality habitats predominantly distributed in the central areas characterized by severe salinization, interspersed with patches of good-quality habitat. (2) The degradation of habitat quality was mainly concentrated in natural land cover types, whereas improvements were observed locally in farmland and bare land. However, slight opposite trends were detected between the mean habitat quality values and the habitat change areas in forests, waters, and bare land. As the elevation continuously increased, the habitat quality grade shifted towards better conditions. (3) A spatial autocorrelation analysis revealed a significant clustering of habitat quality, but the extent of hot spots and cold spots gradually shrank as grassland degradation and saline land management progressed. By incorporating dynamic threat factors and integrating multi-source data, this study improved the habitat quality assessment framework for semi-arid regions and provided scientific support for spatially stratified conservation strategies. Full article
(This article belongs to the Special Issue Temporal and Spatial Analysis of Multi-Source Remote Sensing Images)
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26 pages, 9328 KiB  
Article
Global Optical and SAR Image Registration Method Based on Local Distortion Division
by Bangjie Li, Dongdong Guan, Yuzhen Xie, Xiaolong Zheng, Zhengsheng Chen, Lefei Pan, Weiheng Zhao and Deliang Xiang
Remote Sens. 2025, 17(9), 1642; https://doi.org/10.3390/rs17091642 - 6 May 2025
Viewed by 208
Abstract
Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. This effect is particularly pronounced between optical and SAR images, where the range-based imaging mechanism of SAR sensors leads to significant local geometric distortions, such [...] Read more.
Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. This effect is particularly pronounced between optical and SAR images, where the range-based imaging mechanism of SAR sensors leads to significant local geometric distortions, such as perspective shrinkage and occlusion. As a result, it becomes difficult to represent the spatial correspondence between optical and SAR images using a single geometric model. To address this challenge, we propose a global optical-SAR image registration method that leverages local distortion characteristics. Specifically, we introduce a Superpixel-based Local Distortion Division (SLDD) method, which defines superpixel region features and segments the image into local distortion and normal regions by computing the Mahalanobis distance between superpixel features. We further design a Multi-Feature Fusion Capsule Network (MFFCN) that integrates shallow salient features with deep structural details, reconstructing the dimensions of digital capsules to generate feature descriptors encompassing texture, phase, structure, and amplitude information. This design effectively mitigates the information loss and feature degradation problems caused by pooling operations in conventional convolutional neural networks (CNNs). Additionally, a hard negative mining loss is incorporated to further enhance feature discriminability. Feature descriptors are extracted separately from regions with different distortion levels, and corresponding transformation models are built for local registration. Finally, the local registration results are fused to generate a globally aligned image. Experimental results on public datasets demonstrate that the proposed method achieves superior performance over state-of-the-art (SOTA) approaches in terms of Root Mean Squared Error (RMSE), Correct Match Number (CMN), Distribution of Matched Points (Scat), Edge Fidelity (EF), and overall visual quality. Full article
(This article belongs to the Special Issue Temporal and Spatial Analysis of Multi-Source Remote Sensing Images)
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21 pages, 5384 KiB  
Article
A Video SAR Multi-Target Tracking Algorithm Based on Re-Identification Features and Multi-Stage Data Association
by Anxi Yu, Boxu Wei, Wenhao Tong, Zhihua He and Zhen Dong
Remote Sens. 2025, 17(6), 959; https://doi.org/10.3390/rs17060959 - 8 Mar 2025
Viewed by 794
Abstract
Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant [...] Read more.
Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data. Full article
(This article belongs to the Special Issue Temporal and Spatial Analysis of Multi-Source Remote Sensing Images)
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20 pages, 7676 KiB  
Article
A High-Precision Matching Method for Heterogeneous SAR Images Based on ROEWA and Angle-Weighted Gradient
by Anxi Yu, Wenhao Tong, Zhengbin Wang, Keke Zhang and Zhen Dong
Remote Sens. 2025, 17(5), 749; https://doi.org/10.3390/rs17050749 - 21 Feb 2025
Viewed by 327
Abstract
The prerequisite for the fusion processing of heterogeneous SAR images lies in high-precision image matching, which can be widely applied in areas such as geometric localization, scene matching navigation, and target recognition. This study proposes a method for high-precision matching of heterogeneous SAR [...] Read more.
The prerequisite for the fusion processing of heterogeneous SAR images lies in high-precision image matching, which can be widely applied in areas such as geometric localization, scene matching navigation, and target recognition. This study proposes a method for high-precision matching of heterogeneous SAR images based on the combination of the single-scale ratio of an exponentially weighted averages (ROEWA) operator and angle-weighted gradient (RAWG). The method consists of the following three main steps: feature point extraction, feature description, and feature matching. The algorithm utilizes the block-based SAR-Harris operator to extract feature points from the reference SAR image, effectively combating the interference of coherent speckle noise and improving the uniformity of feature point distribution. By employing the single-scale ROEWA operator in conjunction with angle-weighted gradient projection, the construction of a 3D dense feature descriptor is achieved, enhancing the consistency of gradient features in heterogeneous SAR images and smoothing the search surface. Through the optimal feature construction strategy and frequency domain SSD algorithm, fast template matching is realized. Experimental comparisons with other mainstream matching methods demonstrate that the Root Mean Square Error (RMSE) of our method is reduced by 47.5% compared with CFOG, and compared with HOPES, the error is reduced by 15.4% and the matching time is reduced by 34.3%. The proposed approach effectively addresses the nonlinear intensity differences, geometric disparities, and interference of coherent speckle noise in heterogeneous SAR images. It exhibits robustness, high precision, and efficiency as its prominent advantages. Full article
(This article belongs to the Special Issue Temporal and Spatial Analysis of Multi-Source Remote Sensing Images)
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