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Remote Sens., Volume 17, Issue 20 (October-2 2025) – 9 articles

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28 pages, 65254 KB  
Article
SAM-Based Few-Shot Learning for Coastal Vegetation Segmentation in UAV Imagery via Cross-Matching and Self-Matching
by Yunfan Wei, Zhiyou Guo, Conghui Li, Weiran Li and Shengke Wang
Remote Sens. 2025, 17(20), 3404; https://doi.org/10.3390/rs17203404 - 10 Oct 2025
Abstract
Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations [...] Read more.
Coastal zones, as critical intersections of ecosystems, resource utilization, and socioeconomic activities, exhibit complex and diverse land cover types with frequent changes. Acquiring large-scale, high-quality annotated data in these areas is costly and time-consuming, which makes rule-based segmentation methods reliant on extensive annotations impractical. Few-shot semantic segmentation, which enables effective generalization from limited labeled samples, thus becomes essential for coastal region analysis. In this work, we propose an optimized few-shot segmentation method based on the Segment Anything Model (SAM) with a frozen-parameter segmentation backbone to improve generalization. To address the high visual similarity among coastal vegetation classes, we design a cross-matching module integrated with a hyper-correlation pyramid to enhance fine-grained visual correspondence. Additionally, a self-matching module is introduced to mitigate scale variations caused by UAV altitude changes. Furthermore, we construct a novel few-shot segmentation dataset, OUC-UAV-SEG-2i, based on the OUC-UAV-SEG dataset, to alleviate data scarcity. In quantitative experiments, the suggested approach outperforms existing models in mIoU and FB-IoU under ResNet50/101 (e.g., ResNet50’s 1-shot/5-shot mIoU rises by 4.69% and 4.50% vs. SOTA), and an ablation study shows adding CMM, SMM, and SAM boosts Mean mIoU by 4.69% over the original HSNet, significantly improving few-shot semantic segmentation performance. Full article
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34 pages, 5368 KB  
Article
Next-Generation Drought Forecasting: Hybrid AI Models for Climate Resilience
by Jinping Liu, Tie Liu, Lei Huang, Yanqun Ren and Panxing He
Remote Sens. 2025, 17(20), 3402; https://doi.org/10.3390/rs17203402 - 10 Oct 2025
Abstract
Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience—especially in semi-arid regions like the Inner Mongolia segment of China’s Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution [...] Read more.
Droughts are increasingly threatening ecological balance, agricultural productivity, and socio-economic resilience—especially in semi-arid regions like the Inner Mongolia segment of China’s Yellow River Basin. This study presents a hybrid drought forecasting framework integrating machine learning (ML) and deep learning (DL) models with high-resolution historical and downscaled future climate data. TerraClimate observations (1985–2014) and bias-corrected CMIP6 projections (2030–2050) under SSP2-4.5 and SSP5-8.5 scenarios were utilized to develop and evaluate the models. Among the tested ML algorithms, Random Forest (RF) demonstrated the best trade-off between accuracy and interpretability and was selected for feature importance analysis. The top-ranked predictors—precipitation, solar radiation, and maximum temperature—were used to train a Long Short-Term Memory (LSTM) network. The LSTM outperformed all ML models, achieving high predictive skill (R2 = 0.766, CC = 0.880, RMSE = 0.885). Scenario-based projections revealed increasing drought severity and variability under SSP5-8.5, with mean PDSI values dropping below −3 after 2040 and deepening toward −4 by 2049. The high-emission scenario also exhibited broader uncertainty bands and amplified interannual anomalies. These findings highlight the value of hybrid AI–climate modeling approaches in capturing complex drought dynamics and supporting anticipatory water resource planning in vulnerable dryland environments. Full article
(This article belongs to the Section Environmental Remote Sensing)
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28 pages, 37439 KB  
Article
Structural Health Monitoring of Anaerobic Lagoon Floating Covers Using UAV-Based LiDAR and Photogrammetry
by Benjamin Steven Vien, Thomas Kuen, Louis Raymond Francis Rose and Wing Kong Chiu
Remote Sens. 2025, 17(20), 3401; https://doi.org/10.3390/rs17203401 (registering DOI) - 10 Oct 2025
Abstract
There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at [...] Read more.
There has been significant interest in deploying unmanned aerial vehicles (UAVs) for their ability to perform precise and rapid remote mapping and inspection of critical environmental assets for structural health monitoring. This case study investigates the use of UAV-based LiDAR and photogrammetry at Melbourne Water’s Western Treatment Plant (WTP) to routinely monitor high-density polyethylene floating covers on anaerobic lagoons. The proposed approach integrates LiDAR and photogrammetry data to enhance the accuracy and efficiency of generating digital elevation models (DEMs) and orthomosaics by leveraging the strengths of both methods. Specifically, the photogrammetric images were orthorectified onto LiDAR-derived DEMs as the projection plane to construct the corresponding orthomosaic. This method captures precise elevation points directly from LiDAR, forming a robust foundation dataset for DEM construction. This streamlines the workflow without compromising detail, as it eliminates the need for time-intensive photogrammetry processes, such as dense cloud and depth map generation. This integration accelerates dataset production by up to four times compared to photogrammetry alone, while achieving centimetre-level accuracy. The LiDAR-derived DEM achieved higher elevation accuracy with a root mean square error (RMSE) of 56.1 mm, while the photogrammetry-derived DEM achieved higher in-plane accuracy with an RMSE of up to 35.4 mm. An analysis of cover deformation revealed that the floating cover had elevated rapidly within the first two years post-installation before showing lateral displacement around the sixth year, which was also evident from a significant increase in wrinkling. This approach delivers valuable insights into cover condition that, in turn, clarifies scum accumulation and movement, thereby enhancing structural integrity management and supporting environmental sustainability at WTP by safeguarding methane-rich biogas for renewable-energy generation and controlling odours. The findings support the ongoing collaborative industry research between Monash University and Melbourne Water, aimed at achieving comprehensive structural and prognostic health assessments of these high-value assets. Full article
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18 pages, 7157 KB  
Article
Perspective Back-Projection Algorithm: Interface Imaging for Airborne Ice Detection
by Yingge Wang, Jinbiao Zhu, Jie Pan and Yuquan Liu
Remote Sens. 2025, 17(20), 3400; https://doi.org/10.3390/rs17203400 - 10 Oct 2025
Abstract
The deployment of traditional ground-penetrating radar (GPR) systems for ice detection on steep terrain presents substantial safety challenges for ground crews due to inaccessibility and hazardous working conditions. However, airborne GPR (AGPR) and radio echo sounding (RES) provide solutions to these difficulties. Assuming [...] Read more.
The deployment of traditional ground-penetrating radar (GPR) systems for ice detection on steep terrain presents substantial safety challenges for ground crews due to inaccessibility and hazardous working conditions. However, airborne GPR (AGPR) and radio echo sounding (RES) provide solutions to these difficulties. Assuming that ice is homogeneous, we introduce a perspective back-projection algorithm designed to process AGPR or RES data that directly searches for unobstructed refracted electromagnetic (EM) wave paths and focuses EM energy below the surface by computing path-specific travel times. The results from the 2D and 3D imaging tests indicate that the perspective back-projection algorithm can accurately image the ice–rock interface. However, Snell’s Law suggests that part of the energy may fail to propagate through the air–ice interface and reach either the ice–rock interface or the receivers in scenarios where the incident angle of an EM wave exceeds a certain threshold. This energy deficit can hinder the perspective back-projection algorithm from accurately imaging such ice–rock interfaces. Despite these limitations, the perspective back-projection algorithm remains a promising tool for imaging sub-ice interfaces in AGPR and RES ice detection. Full article
(This article belongs to the Special Issue Electromagnetic Modeling of Geophysical Prospecting in Remote Sensing)
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30 pages, 11330 KB  
Article
Distance Transform-Based Spatiotemporal Model for Approximating Missing NDVI from Satellite Data
by Amirhossein Mirtabatabaeipour, Lakin Wecker, Majid Amirfakhrian and Faramarz F. Samavati
Remote Sens. 2025, 17(20), 3399; https://doi.org/10.3390/rs17203399 - 10 Oct 2025
Abstract
One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns [...] Read more.
One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article
(This article belongs to the Special Issue Big Geo-Spatial Data and Advanced 3D Modelling in GIS and Satellite)
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21 pages, 14964 KB  
Article
An Automated Framework for Abnormal Target Segmentation in Levee Scenarios Using Fusion of UAV-Based Infrared and Visible Imagery
by Jiyuan Zhang, Zhonggen Wang, Jing Chen, Fei Wang and Lyuzhou Gao
Remote Sens. 2025, 17(20), 3398; https://doi.org/10.3390/rs17203398 - 10 Oct 2025
Abstract
Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. [...] Read more.
Levees are critical for flood defence, but their integrity is threatened by hazards such as piping and seepage, especially during high-water-level periods. Traditional manual inspections for these hazards and associated emergency response elements, such as personnel and assets, are inefficient and often impractical. While UAV-based remote sensing offers a promising alternative, the effective fusion of multi-modal data and the scarcity of labelled data for supervised model training remain significant challenges. To overcome these limitations, this paper reframes levee monitoring as an unsupervised anomaly detection task. We propose a novel, fully automated framework that unifies geophysical hazards and emergency response elements into a single analytical category of “abnormal targets” for comprehensive situational awareness. The framework consists of three key modules: (1) a state-of-the-art registration algorithm to precisely align infrared and visible images; (2) a generative adversarial network to fuse the thermal information from IR images with the textural details from visible images; and (3) an adaptive, unsupervised segmentation module where a mean-shift clustering algorithm, with its hyperparameters automatically tuned by Bayesian optimization, delineates the targets. We validated our framework on a real-world dataset collected from a levee on the Pajiang River, China. The proposed method demonstrates superior performance over all baselines, achieving an Intersection over Union of 0.348 and a macro F1-Score of 0.479. This work provides a practical, training-free solution for comprehensive levee monitoring and demonstrates the synergistic potential of multi-modal fusion and automated machine learning for disaster management. Full article
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22 pages, 4487 KB  
Article
A Trajectory Estimation Method Based on Microwave Three-Point Ranging for Sparse 3D Radar Imaging
by Changyu Lou, Jingcheng Zhao, Xingli Wu, Zongkai Yang, Jungang Miao and Tao Hong
Remote Sens. 2025, 17(20), 3397; https://doi.org/10.3390/rs17203397 - 10 Oct 2025
Abstract
Precise estimate of antenna location is essential for high-quality three-dimensional (3D) radar imaging, especially under sparse sampling schemes. In scenarios involving synchronized scanning and rotational motion, small deviations in the radar’s transmitting position can lead to significant phase errors, thereby degrading image fidelity [...] Read more.
Precise estimate of antenna location is essential for high-quality three-dimensional (3D) radar imaging, especially under sparse sampling schemes. In scenarios involving synchronized scanning and rotational motion, small deviations in the radar’s transmitting position can lead to significant phase errors, thereby degrading image fidelity or even causing image failure. To address this challenge, we propose a novel trajectory estimation method based on microwave three-point ranging. The method utilizes three fixed microwave-reflective calibration spheres positioned outside the imaging scene. By measuring the one-dimensional radial distances between the radar and each of the three spheres, and geometrically constructing three intersecting spheres in space, the radar’s spatial position can be uniquely determined at each sampling moment. This external reference-based localization scheme significantly reduces positioning errors without requiring precise synchronization control between scanning and rotation. Furthermore, the proposed approach enhances the robustness and flexibility of sparse sampling strategies in near-field radar imaging. Beyond ground-based setups, the method also holds promise for drone-borne 3D imaging applications, enabling accurate localization of onboard radar systems during flight. Simulation results and error analysis demonstrate that the proposed method improves trajectory accuracy and supports high-fidelity 3D reconstruction under non-ideal sampling conditions. Full article
(This article belongs to the Section Engineering Remote Sensing)
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19 pages, 2742 KB  
Article
Cloud-Based Solutions for Monitoring Coastal Ecosystems and the Prioritization of Restoration Efforts Across Belize
by Christine Evans, Lauren Carey, Florencia Guerra, Emil A. Cherrington, Edgar Correa and Diego Quintero
Remote Sens. 2025, 17(20), 3396; https://doi.org/10.3390/rs17203396 - 10 Oct 2025
Abstract
In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of [...] Read more.
In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article
(This article belongs to the Special Issue Remote Sensing in Mangroves IV)
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1 pages, 116 KB  
Retraction
RETRACTED: Zhang et al. BIM Data Model Based on Multi-Scale Grids in Civil Engineering Buildings. Remote Sens. 2024, 16, 690
by Huangchuang Zhang, Ge Li and Meilin Pu
Remote Sens. 2025, 17(20), 3395; https://doi.org/10.3390/rs17203395 - 10 Oct 2025
Abstract
The journal retracts the article titled “BIM Data Model Based on Multi-Scale Grids in Civil Engineering Buildings” [...] Full article
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