Remote Sensing

36 pages, 5941 KB

Open AccessReview

Physics-Driven SAR Target Detection: A Review and Perspective

by Xinyi Li, Lei Liu, Gang Wan, Fengjie Zheng, Shihao Guo, Guangde Sun, Ziyan Wang and Xiaoxuan Liu

Remote Sens. 2026, 18(2), 200; https://doi.org/10.3390/rs18020200 - 7 Jan 2026

Abstract

Synthetic Aperture Radar (SAR) is highly valuable for target detection due to its all-weather, day-night operational capability and certain ground penetration potential. However, traditional SAR target detection methods often directly adapt algorithms designed for optical imagery, simplistically treating SAR data as grayscale images. [...] Read more.

Synthetic Aperture Radar (SAR) is highly valuable for target detection due to its all-weather, day-night operational capability and certain ground penetration potential. However, traditional SAR target detection methods often directly adapt algorithms designed for optical imagery, simplistically treating SAR data as grayscale images. This approach overlooks SAR’s unique physical nature, failing to account for key factors such as backscatter variations from different polarizations, target representation changes across resolutions, and detection threshold shifts due to clutter background heterogeneity. Consequently, these limitations lead to insufficient cross-polarization adaptability, feature masking, and degraded recognition accuracy due to clutter interference. To address these challenges, this paper systematically reviews recent research advances in SAR target detection, focusing on physical constraints including polarization characteristics, scattering mechanisms, signal-domain properties, and resolution effects. Finally, it outlines promising research directions to guide future developments in physics-aware SAR target detection. Full article

(This article belongs to the Special Issue Advanced Methods and Applications in SAR (Synthetic Aperture Radar) Image Target Detection and Recognition)

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23 pages, 10505 KB

Open AccessArticle

SSGTN: Spectral–Spatial Graph Transformer Network for Hyperspectral Image Classification

by Haotian Shi, Zihang Luo, Yiyang Ma, Guanquan Zhu and Xin Dai

Remote Sens. 2026, 18(2), 199; https://doi.org/10.3390/rs18020199 - 7 Jan 2026

Abstract

Hyperspectral image (HSI) classification is fundamental to a wide range of remote sensing applications, such as precision agriculture, environmental monitoring, and urban planning, because HSIs provide rich spectral signatures that enable the discrimination of subtle material differences. Deep learning approaches, including Convolutional Neural [...] Read more.

Hyperspectral image (HSI) classification is fundamental to a wide range of remote sensing applications, such as precision agriculture, environmental monitoring, and urban planning, because HSIs provide rich spectral signatures that enable the discrimination of subtle material differences. Deep learning approaches, including Convolutional Neural Networks (CNNs), Graph Convolutional Networks (GCNs), and Transformers, have achieved strong performance in learning spatial–spectral representations. However, these models often face difficulties in jointly modeling long-range dependencies, fine-grained local structures, and non-Euclidean spatial relationships, particularly when labeled training data are scarce. This paper proposes a Spectral–Spatial Graph Transformer Network (SSGTN), a dual-branch architecture that integrates superpixel-based graph modeling with Transformer-based global reasoning. SSGTN consists of four key components, namely (1) an LDA-SLIC superpixel graph construction module that preserves discriminative spectral–spatial structures while reducing computational complexity, (2) a lightweight spectral denoising module based on

1 \times 1

convolutions and batch normalization to suppress redundant and noisy bands, (3) a Spectral–Spatial Shift Module (SSSM) that enables efficient multi-scale feature fusion through channel-wise and spatial-wise shift operations, and (4) a dual-branch GCN-Transformer block that jointly models local graph topology and global spectral–spatial dependencies. Extensive experiments on three public HSI datasets (Indian Pines, WHU-Hi-LongKou, and Houston2018) under limited supervision (1% training samples) demonstrate that SSGTN consistently outperforms state-of-the-art CNN-, Transformer-, Mamba-, and GCN-based methods in overall accuracy, Average Accuracy, and the

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coefficient. The proposed framework provides an effective baseline for HSI classification under limited supervision and highlights the benefits of integrating graph-based structural priors with global contextual modeling. Full article

(This article belongs to the Special Issue Hyperspectral Classification in Remote Sensing: State-of-the-Art Methods and Emerging Trends)

30 pages, 6797 KB

Open AccessArticle

Voxel-Based Leaf Area Estimation in Trellis-Grown Grapevines: A Destructive Validation and Comparison with Optical LAI Methods

by Poching Teng, Hiroyoshi Sugiura, Tomoki Date, Unseok Lee, Takeshi Yoshida, Tomohiko Ota and Junichi Nakagawa

Remote Sens. 2026, 18(2), 198; https://doi.org/10.3390/rs18020198 - 7 Jan 2026

Abstract

This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during [...] Read more.

This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during the growing seasons, and destructive leaf sampling was conducted to quantify true leaf area across multiple vines and years. After removing non-leaf structures with ExGR filtering, the point clouds were voxelized at a 1 cm³ resolution to derive structural occupancy metrics. Voxel-based leaf area showed strong within-vine correlations with destructively measured values (R² = 0.77–0.95), while cross-vine variability was influenced by canopy complexity, illumination, and point-cloud density. In contrast, optical LAI tools (DHP and LAI–2000) exhibited negligible correspondence with true leaf area due to multilayer occlusion and lateral light contamination typical of pergola systems. This expanded, multi-year analysis demonstrates that voxel occupancy provides a robust and scalable indicator of canopy structural density and leaf area, offering a practical foundation for remote-sensing-based phenotyping, yield estimation, and data-driven management in perennial fruit crops. Full article

(This article belongs to the Section Forest Remote Sensing)

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35 pages, 10379 KB

Open AccessArticle

Research on Multi-Stage Optimization for High-Precision Digital Surface Model and True Digital Orthophoto Map Generation Methods

by Yingwei Ge, Renke Ji, Bingxuan Guo, Qinsi Wang, Xiao Jiang and Mofei Chen

Remote Sens. 2026, 18(2), 197; https://doi.org/10.3390/rs18020197 - 7 Jan 2026

Abstract

To enhance the overall quality and consistency of depth maps, Digital Surface Models (DSM), and True Digital Orthophoto Map (TDOM) in UAV image reconstruction, this paper proposes a multi-stage adaptive optimization generation method. First, to address the noise and outlier issues in depth [...] Read more.

To enhance the overall quality and consistency of depth maps, Digital Surface Models (DSM), and True Digital Orthophoto Map (TDOM) in UAV image reconstruction, this paper proposes a multi-stage adaptive optimization generation method. First, to address the noise and outlier issues in depth maps, an adaptive joint bilateral filtering-based optimization method is introduced. This method repairs anomalous depth values using a four-directional filling strategy, incorporates image-guided joint bilateral filtering to enhance edge structure representation, effectively improving the accuracy and continuity of the depth map. Next, during the DSM generation stage, a method based on depth value voting space and elevation anomaly detection is proposed. A joint mechanism of elevation calculation and anomaly point detection is used to suppress noise and errors, while a height value completion strategy significantly enhances the geometric accuracy and integrity of the DSM. Finally, in the TDOM generation process, occlusion detection and gap-line generation techniques are introduced. Together with uniform lighting, color adjustment, and image gap optimization strategies, this improves texture stitching continuity and brightness consistency, effectively reducing artifacts caused by gaps, blurriness, and lighting differences. Experimental results show that the proposed method significantly improves depth map smoothness, DSM geometric accuracy, and TDOM visual consistency compared to traditional methods, providing a complete and efficient technical pathway for high-quality surface reconstruction. Full article

(This article belongs to the Special Issue Remote Sensing for 2D/3D Mapping)

29 pages, 3983 KB

Open AccessReview

A Dive into Generative Adversarial Networks in the World of Hyperspectral Imaging: A Survey of the State of the Art

by Pallavi Ranjan, Ankur Nandal, Saurabh Agarwal and Rajeev Kumar

Remote Sens. 2026, 18(2), 196; https://doi.org/10.3390/rs18020196 - 6 Jan 2026

Abstract

Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient [...] Read more.

Hyperspectral imaging (HSI) captures rich spectral information across a wide range of wavelengths, enabling advanced applications in remote sensing, environmental monitoring, medical diagnosis, and related domains. However, the high dimensionality, spectral variability, and inherent noise of HSI data present significant challenges for efficient processing and reliable analysis. In recent years, Generative Adversarial Networks (GANs) have emerged as transformative deep learning paradigms, demonstrating strong capabilities in data generation, augmentation, feature learning, and representation modeling. Consequently, the integration of GANs into HSI analysis has gained substantial research attention, resulting in a diverse range of architectures tailored to HSI-specific tasks. Despite these advances, existing survey studies often focus on isolated problems or individual application domains, limiting a comprehensive understanding of the broader GAN–HSI landscape. To address this gap, this paper presents a comprehensive review of GAN-based hyperspectral imaging research. The review systematically examines the evolution of GAN–HSI integration, categorizes representative GAN architectures, analyzes domain-specific applications, and discusses commonly adopted hyperparameter tuning strategies. Furthermore, key research challenges and open issues are identified, and promising future research directions are outlined. This synergy addresses critical hyperspectral data analysis challenges while unlocking transformative innovations across multiple sectors. Full article

(This article belongs to the Special Issue Trends and Prospects in Hyperspectral Remote Sensing Images Processing and Analysis)

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32 pages, 44207 KB

Open AccessArticle

Is Satellite-Derived Bathymetry Vertical Accuracy Dependent on Satellite Mission and Processing Method?

by Monica Palaseanu-Lovejoy, Jeffrey Danielson, Minsu Kim, Bryan Eder, Gretchen Imahori and Curt Storlazzi

Remote Sens. 2026, 18(2), 195; https://doi.org/10.3390/rs18020195 - 6 Jan 2026

Abstract

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; [...] Read more.

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all SDB methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the Iterative Closest Point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error (RMSE). Thus, it is recommended to perform detrending where possible to achieve an error distribution as close to normality as possible for which error metrics are more diagnostic. Full article

30 pages, 9375 KB

Open AccessArticle

Analysis of Temporal Cumulative, Lagging Effects and Driving Mechanisms of Environmental Factors on Green Tide Outbreaks: A Case Study of the Ulva Prolifera Disaster in the South Yellow Sea, China

by Zhen Tian, Jianhua Zhu, Huimin Zou, Zeen Lu, Yating Zhan, Weiwei Li, Bangping Deng, Lijia Liu and Xiucheng Yu

Remote Sens. 2026, 18(2), 194; https://doi.org/10.3390/rs18020194 - 6 Jan 2026

Abstract

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between [...] Read more.

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

26 pages, 6277 KB

Open AccessArticle

Enhancing Hydrological Model Calibration for Flood Prediction in Dam-Regulated Basins with Satellite-Derived Reservoir Dynamics

by Chaoqun Li, Huan Wu, Lorenzo Alfieri, Yiwen Mei, Nergui Nanding, Zhijun Huang, Ying Hu and Lei Qu

Remote Sens. 2026, 18(2), 193; https://doi.org/10.3390/rs18020193 - 6 Jan 2026

Abstract

The construction and operation of reservoirs have made hydrological processes complex, posing challenges to flood modeling. While many hydrological models have incorporated reservoir operation schemes to improve discharge estimation, the influence of reservoir representation on model calibration has not been sufficiently evaluated—an issue [...] Read more.

The construction and operation of reservoirs have made hydrological processes complex, posing challenges to flood modeling. While many hydrological models have incorporated reservoir operation schemes to improve discharge estimation, the influence of reservoir representation on model calibration has not been sufficiently evaluated—an issue that fundamentally affects the spatial reliability of distributed modeling. Additionally, the limited availability of reservoir regulation data impedes dam-inclusive flood simulation. To overcome these limitations, this study proposes a synergistic modeling framework for data-scarce dammed basins. It integrates a satellite-based reservoir operation scheme into a distributed hydrological model and incorporates reservoir processes into the model calibration procedure. The framework was tested using the coupled version of the DRIVE flood model (DRIVE-Dam) in the Nandu River Basin, southern China. Two calibration configurations, with and without dam operation (CWD vs. CWOD), were compared. Results show that reservoir dynamics were effectively reconstructed by combining satellite altimetry with FABDEM topography, successfully supporting the development of the reservoir scheme. Multi-site comparisons indicate that, while CWD slightly improved streamflow estimation (NSE and KGE > 0.75, similar to CWOD) on the calibrated outlet gauge, it enhanced basin-internal process representation, as evidenced by the superior peak discharge and flood event capture with reduced bias, boosting flood detection probability from 0.54 to 0.60 and reducing false alarms from 0.28 to 0.15. The improvements stem from refined parameterization enabled by a physically complete model structure. In contrast, CWOD leads to subdued flood impulses and prolonged recession due to spurious parameters that distort baseflow and runoff response. The proposed methodology provides a practical reference for flood forecasting in dam-regulated basins, demonstrating that reservoir representation enhances model parameterization and underscoring the strong potential of satellite observations for hydrological modeling in data-limited regions. Full article

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24 pages, 3401 KB

Open AccessArticle

Ground to Altitude: Weakly-Supervised Cross-Platform Domain Generalization for LiDAR Semantic Segmentation

by Jingyi Wang, Xiaojia Xiang, Jun Lai, Yu Liu, Qi Li and Chen Chen

Remote Sens. 2026, 18(2), 192; https://doi.org/10.3390/rs18020192 - 6 Jan 2026

Abstract

Collaborative sensing between low-altitude remote sensing and ground-based mobile mapping lays the theoretical foundation for multi-platform 3D data fusion. However, point clouds collected from Airborne Laser Scanners (ALSs) remain scarce due to high acquisition and annotation costs. In contrast, while autonomous driving datasets [...] Read more.

Collaborative sensing between low-altitude remote sensing and ground-based mobile mapping lays the theoretical foundation for multi-platform 3D data fusion. However, point clouds collected from Airborne Laser Scanners (ALSs) remain scarce due to high acquisition and annotation costs. In contrast, while autonomous driving datasets are more accessible, dense annotation remains a significant bottleneck. To address this, we propose Ground to Altitude (GTA), a weakly supervised domain generalization (DG) framework. GTA leverages sparse autonomous driving data to learn robust representations, enabling reliable segmentation on airborne point clouds under zero-label conditions. Specifically, we tackle cross-platform discrepancies through progressive domain-aware augmentation (PDA) and cross-scale semantic alignment (CSA). For PDA, we design a distance-guided dynamic upsampling strategy to approximate airborne point density and a cross-view augmentation scheme to model viewpoint variations. For CSA, we impose cross-domain feature consistency and contrastive regularization to enhance robustness against perturbations. A progressive training pipeline is further employed to maximize the utility of limited annotations and abundant unlabeled data. Our study reveals the limitations of existing DG methods in cross-platform scenarios. Extensive experiments demonstrate that GTA achieves state-of-the-art (SOTA) performance. Notably, under the challenging 0.1% supervision setting, our method achieves a 6.36% improvement in mIoU over the baseline on the SemanticKITTI → DALES benchmark, demonstrating significant gains across diverse categories beyond just structural objects. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Fourth Edition))

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20 pages, 15504 KB

Open AccessArticle

O-Transformer-Mamba: An O-Shaped Transformer-Mamba Framework for Remote Sensing Image Haze Removal

by Xin Guan, Runxu He, Le Wang, Hao Zhou, Yun Liu and Hailing Xiong

Remote Sens. 2026, 18(2), 191; https://doi.org/10.3390/rs18020191 - 6 Jan 2026

Abstract

Although Transformer-based and state-space models (e.g., Mamba) have demonstrated impressive performance in image restoration, they remain deficient in remote sensing image dehazing. Transformer-based models tend to distribute attention evenly, making them difficult to handle the uneven distribution of haze. While Mamba excels at [...] Read more.

Although Transformer-based and state-space models (e.g., Mamba) have demonstrated impressive performance in image restoration, they remain deficient in remote sensing image dehazing. Transformer-based models tend to distribute attention evenly, making them difficult to handle the uneven distribution of haze. While Mamba excels at modeling long-range dependencies, it lacks fine-grained spatial awareness of complex atmospheric scattering. To overcome these limitations, we present a new O-shaped dehazing architecture that combines a Sparse-Enhanced Self-Attention (SE-SA) module with a Mixed Visual State Space Model (Mix-VSSM), balancing haze-sensitive details in remote sensing images with long-range context modeling. The SE-SA module introduces a dynamic soft masking mechanism that adaptively adjusts attention weights based on the local haze distribution, enabling the network to more effectively focus on severely degraded regions while suppressing redundant responses. Furthermore, the Mix-VSSM enhances global context modeling by combining sequential processing of 2D perception with local residual information. This design mitigates the loss of spatial detail in the standard VSSM and improves the feature representation of haze-degraded remote sensing images. Thorough experiments verify that our O-shaped framework outperforms existing methods on several benchmark datasets. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Enhancement)

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26 pages, 18928 KB

Open AccessArticle

Temperature and Moisture Variability Drive Resilience Shifts in Canada’s Undisturbed Forests During 2001–2018

by Chenlin Yang, Tianxiang Cui, Lei Fan, Jian Wang and Jean-Pierre Wigneron

Remote Sens. 2026, 18(2), 190; https://doi.org/10.3390/rs18020190 - 6 Jan 2026

Abstract

Canada’s forests are a critical component of the global carbon pool and play an essential role in regulating the Earth’s climate. Since 2000, increasing disturbances have reduced ecosystem resilience, raising concerns about the long-term carbon sequestration capacity of Canada’s forests. Yet, the resilience [...] Read more.

Canada’s forests are a critical component of the global carbon pool and play an essential role in regulating the Earth’s climate. Since 2000, increasing disturbances have reduced ecosystem resilience, raising concerns about the long-term carbon sequestration capacity of Canada’s forests. Yet, the resilience of Canada’s undisturbed forests remains poorly understood. In this study, we assessed resilience across undisturbed forests from 2001 to 2018 by applying the lag-1 autocorrelation (AR(1)) metric to leaf area index (LAI) time series. Our analyses revealed a widespread and substantial temporal shift in resilience from declining to increasing despite a persistently greening trend. These resilience transitions were most pronounced in mixed-species and intermediate-aged forests. By quantifying the influence of multiple environmental drivers, we found that variability in temperature and moisture exerted dominant controls on resilience shifts. Cooler conditions and higher moisture availability contributed to increased resilience, with the largest resilience shifts occurring in regions experiencing sustained cooling or wetting trends. These findings imply that conservation strategies favoring mixed-species and intermediate-aged forests under cooler, wetter conditions could promote long-term ecosystem stability. Full article

(This article belongs to the Special Issue Satellite Soil Moisture Estimation, Assessment, and Applications (Second Edition))

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41 pages, 25791 KB

Open AccessArticle

TGDHTL: Hyperspectral Image Classification via Transformer–Graph Convolutional Network–Diffusion with Hybrid Domain Adaptation

by Zarrin Mahdavipour, Nashwan Alromema, Abdolraheem Khader, Ghulam Farooque, Ali Ahmed and Mohamed A. Damos

Remote Sens. 2026, 18(2), 189; https://doi.org/10.3390/rs18020189 - 6 Jan 2026

Abstract

Hyperspectral image (HSI) classification is pivotal for remote sensing applications, including environmental monitoring, precision agriculture, and urban land-use analysis. However, its accuracy is often limited by scarce labeled data, class imbalance, and domain discrepancies between standard RGB and HSI imagery. Although recent deep [...] Read more.

Hyperspectral image (HSI) classification is pivotal for remote sensing applications, including environmental monitoring, precision agriculture, and urban land-use analysis. However, its accuracy is often limited by scarce labeled data, class imbalance, and domain discrepancies between standard RGB and HSI imagery. Although recent deep learning approaches, such as 3D convolutional neural networks (3D-CNNs), transformers, and generative adversarial networks (GANs), show promise, they struggle with spectral fidelity, computational efficiency, and cross-domain adaptation in label-scarce scenarios. To address these challenges, we propose the Transformer–Graph Convolutional Network–Diffusion with Hybrid Domain Adaptation (TGDHTL) framework. This framework integrates domain-adaptive alignment of RGB and HSI data, efficient synthetic data generation, and multi-scale spectral–spatial modeling. Specifically, a lightweight transformer, guided by Maximum Mean Discrepancy (MMD) loss, aligns feature distributions across domains. A class-conditional diffusion model generates high-quality samples for underrepresented classes in only 15 inference steps, reducing labeled data needs by approximately 25% and computational costs by up to 80% compared to traditional 1000-step diffusion models. Additionally, a Multi-Scale Stripe Attention (MSSA) mechanism, combined with a Graph Convolutional Network (GCN), enhances pixel-level spatial coherence. Evaluated on six benchmark datasets including HJ-1A and WHU-OHS, TGDHTL consistently achieves high overall accuracy (e.g., 97.89% on University of Pavia) with just 11.9 GFLOPs, surpassing state-of-the-art methods. This framework provides a scalable, data-efficient solution for HSI classification under domain shifts and resource constraints. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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25 pages, 7922 KB

Open AccessArticle

Generation of Rainfall Maps from GK2A Satellite Images Using Deep Learning

by Yerim Lim, Yeji Choi, Eunbin Kim, Yong-Jae Moon and Hyun-Jin Jeong

Remote Sens. 2026, 18(2), 188; https://doi.org/10.3390/rs18020188 - 6 Jan 2026

Abstract

Accurate rainfall monitoring is essential for mitigating hydrometeorological disasters and understanding hydrological changes under climate change. This study presents a deep learning-based rainfall estimation framework using multispectral GEO-KOMPSAT-2A (GK2A) satellite imagery. The analysis primarily focuses on daytime observations to take advantage of visible [...] Read more.

Accurate rainfall monitoring is essential for mitigating hydrometeorological disasters and understanding hydrological changes under climate change. This study presents a deep learning-based rainfall estimation framework using multispectral GEO-KOMPSAT-2A (GK2A) satellite imagery. The analysis primarily focuses on daytime observations to take advantage of visible channel information, which provides richer representations of cloud characteristics during daylight conditions. The core model, Model-HSP, is built on the Pix2PixCC architecture and trained with Hybrid Surface Precipitation (HSP) data from weather radar. To further enhance accuracy, an ensemble model (Model-ENS) integrates the outputs of Model-HSP and a radar based Model-CMX, leveraging their complementary strengths for improved generalization, robustness, and stability across rainfall regimes. Performance was evaluated over two periods—a one year period from May 2023 to April 2024 and the August 2023 monsoon season—at 2 km and 4 km spatial resolutions, using RMSE and CC as quantitative metrics. Case analyses confirmed the superior capability of Model-ENS in capturing rainfall distribution, intensity, and temporal evolution across diverse weather conditions. These findings show that deep learning greatly enhances GEO satellite rainfall estimation, enabling real-time, high-resolution monitoring even in radar sparse or limited coverage regions, and offering strong potential for global and regional hydrometeorological and climate research applications. Full article

(This article belongs to the Special Issue Advance of Radar Meteorology and Hydrology II)

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25 pages, 21871 KB

Open AccessArticle

Monitoring Dendrolimus punctatus Walker Infestations Using Sentinel-2: A Monthly Time-Series Approach

by Fangxin Meng, Xianlin Qin, Yakui Shao, Xinyu Hu, Feng Jiang, Shuisheng Huang and Linfeng Yu

Remote Sens. 2026, 18(2), 187; https://doi.org/10.3390/rs18020187 - 6 Jan 2026

Abstract

Infestations of Dendrolimus punctatus Walker (D. punctatus) pose significant threats to forest ecosystem health, necessitating accurate and efficient monitoring for sustainable forest management. A monthly monitoring framework integrating spectral bands, vegetation indices, time-series features, meteorological variables, and topographic characteristics was developed. [...] Read more.

Infestations of Dendrolimus punctatus Walker (D. punctatus) pose significant threats to forest ecosystem health, necessitating accurate and efficient monitoring for sustainable forest management. A monthly monitoring framework integrating spectral bands, vegetation indices, time-series features, meteorological variables, and topographic characteristics was developed. First, cloud-free Sentinel-2 composites were generated via median synthesis, and training samples were selected by integrating GF-1/2 data. Subsequently, a Weighted Composite Index (WCI) was constructed through logistic regression to quantitatively classify infestation severity levels. Meanwhile, time-series features extracted from vegetation indices were incorporated to characterize temporal damage dynamics. Finally, Random Forest (RF) models were then trained for monthly monitoring, achieving overall accuracies exceeding 86.9% with Kappa coefficients ranging from 0.825 to 0.858. The Inverted Red Edge Chlorophyll Index (IRECI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) exhibited the highest sensitivity to D. punctatus damage and thus received the greatest weights in the WCI. Time-series features ranked second in importance after vegetation indices, substantially enhancing model performance. Monitoring results from 2019 to 2024 revealed that D. punctatus infestation in Qianshan City exhibited an occurrence pattern progressing from mild to severe and from scattered to aggregated distributions, with major outbreak periods in 2019, 2021, and 2023 reflecting characteristic cyclical dynamics. This study advances existing quantitative monitoring methodologies for D. punctatus and provides technical support and a scientific foundation for precision pest monitoring and forest health management. Full article

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26 pages, 9258 KB

Open AccessArticle

TriGEFNet: A Tri-Stream Multimodal Enhanced Fusion Network for Landslide Segmentation from Remote Sensing Imagery

by Zirui Zhang, Qingfeng Hu, Haoran Fang, Wenkai Liu, Ruimin Feng, Shoukai Chen, Qifan Wu, Peng Wang and Weiqiang Lu

Remote Sens. 2026, 18(2), 186; https://doi.org/10.3390/rs18020186 - 6 Jan 2026

Abstract

Landslides are among the most prevalent geological hazards worldwide, posing severe threats to public safety due to their sudden onset and destructive potential. The rapid and accurate automated segmentation of landslide areas is a critical task for enhancing capabilities in disaster risk assessment, [...] Read more.

Landslides are among the most prevalent geological hazards worldwide, posing severe threats to public safety due to their sudden onset and destructive potential. The rapid and accurate automated segmentation of landslide areas is a critical task for enhancing capabilities in disaster risk assessment, emergency response, and post-disaster management. However, existing deep learning models for landslide segmentation predominantly rely on unimodal remote sensing imagery. In complex Karst landscapes characterized by dense vegetation and severe shadow interference, the optical features of landslides are difficult to extract effectively, thereby significantly limiting recognition accuracy. Therefore, synergistically utilizing multimodal data while mitigating information redundancy and noise interference has emerged as a core challenge in this field. To address this challenge, this paper proposes a Triple-Stream Guided Enhancement and Fusion Network (TriGEFNet), designed to efficiently fuse three data sources: RGB imagery, Vegetation Indices (VI), and Slope. The model incorporates an adaptive guidance mechanism within the encoder. This mechanism leverages the terrain constraints provided by slope to compensate for the information loss within optical imagery under shadowing conditions. Simultaneously, it integrates the sensitivity of VIs to surface destruction to collectively calibrate and enhance RGB features, thereby extracting fused features that are highly responsive to landslides. Subsequently, gated skip connections in the decoder refine these features, ensuring the optimal combination of deep semantic information with critical boundary details, thus achieving deep synergy among multimodal features. A systematic performance evaluation of the proposed model was conducted on the self-constructed Zunyi dataset and two publicly available datasets. Experimental results demonstrate that TriGEFNet achieved mean Intersection over Union (mIoU) scores of 86.27% on the Zunyi dataset, 80.26% on the L4S dataset, and 89.53% on the Bijie dataset, respectively. Compared to the multimodal baseline model, TriGEFNet achieved significant improvements, with maximum gains of 7.68% in Recall and 4.37% in F1-score across the three datasets. This study not only presents a novel and effective paradigm for multimodal remote sensing data fusion but also provides a forward-looking solution for constructing more robust and precise intelligent systems for landslide monitoring and assessment. Full article

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18 pages, 12298 KB

Open AccessArticle

Ancient Burial Mounds Detection in the Altai Mountains with High-Resolution Satellite Images

by Fen Chen, Lu Jin, Jean Bourgeois, Xiangguo Zuo, Tim Van de Voorde, Wouter Gheyle, Timo Balz and Gino Caspari

Remote Sens. 2026, 18(2), 185; https://doi.org/10.3390/rs18020185 - 6 Jan 2026

Abstract

The Altai Mountains rank among the world’s most notable and valuable archaeological regions. Within the sprawling Altai Mountains area, burial mounds (kurgans) of past civilizations, which are sometimes well preserved in permafrost, are a particularly precious trove of archaeological insights. This study investigates [...] Read more.

The Altai Mountains rank among the world’s most notable and valuable archaeological regions. Within the sprawling Altai Mountains area, burial mounds (kurgans) of past civilizations, which are sometimes well preserved in permafrost, are a particularly precious trove of archaeological insights. This study investigates the application of deep learning-based object detection techniques for automatic kurgan identification in high-resolution satellite imagery. We compare the performance of various object detection methods utilizing both convolutional neural network and Transformer backbones. Our results validate the effectiveness of different approaches, especially with larger models, in the challenging task of detecting small archaeological structures. Techniques addressing the class imbalance can further improve performance of off-the-shelf methods. These findings demonstrate the feasibility of employing deep learning techniques to automate kurgan identification, which can improve archaeological surveying processes. It suggests the potential of deep learning technology for constructing a comprehensive inventory of Altai Mountain kurgans, particularly relevant in the context of global warming and archaeological site preservation. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Landscape Archaeology)

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Remote Sens., Volume 18, Issue 2 (January-2 2026) – 16 articles

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