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        <title>Frontiers in Remote Sensing | Image Analysis and Classification section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/remote-sensing/sections/image-analysis-and-classification</link>
        <description>RSS Feed for Image Analysis and Classification section in the Frontiers in Remote Sensing journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-08-16T11:55:44.685+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1919576</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1919576</link>
        <title><![CDATA[DBF-YOLO: a lightweight dual-branch fusion method for Visible-SAR cross-modal remote sensing target detection]]></title>
        <pubdate>2026-08-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaofeng Zhao</author><author>Chenxiao Li</author><author>Hui Zhang</author><author>Kehao Wang</author><author>Fan Zhang</author><author>Hanshuo Huo</author><author>Zhili Zhang</author>
        <description><![CDATA[In visible-SAR cross-modal remote sensing target detection tasks, due to the significant differences between the two modalities in imaging mechanisms and feature representations, as well as the susceptibility of SAR images to speckle noise interference and the inadequate utilization of structural information, existing methods often struggle to balance detection accuracy with model lightweighting and practical deployment requirements. This is particularly true in resource-constrained scenarios, such as space-borne or airborne platforms, where models need to have low parameter counts, reduced computational overhead, and robust adaptability to complex environments. To address these issues, this paper proposes a lightweight dual-branch fusion network, DBF-YOLO, for visible-SAR cross-modal remote sensing target detection. Based on the YOLOv10 framework, the method constructs a visible-SAR dual-branch feature extraction structure and designs an intermediate cross-modal fusion path at three levels (P3, P4, P5) to achieve progressive interaction of dual-modal features at different scales. To overcome the strong speckle noise and edge degradation in SAR images, a SAR Gradient Enhancement Module (SGM) is introduced to enhance the structural representation capability of SAR inputs. Additionally, an Adaptive Gated Dual-Modal Fusion Module (AGD) is proposed to enable dynamic selection and effective complementarity of dual-modal information based on different scales and spatial positions. Experimental results on the OGSOD 1.0 dataset show that DBF-YOLO achieves 94.4% mAP50% and 70.1% mAP50-95, with only 5.0 M parameters and 20.5 GFLOPs, striking a good balance between detection accuracy and computational complexity. Furthermore, experimental results on the OSPRC dataset demonstrate the robustness of DBF-YOLO under different resolutions, polarization modes, and cloud cover conditions. Particularly in low-contrast dense target scenes and cloud cover conditions, the model demonstrates stronger environmental adaptability. This method can provide a reference for designing lightweight multi-modal remote sensing target detection models and deploying them on resource-limited platforms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1852249</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1852249</link>
        <title><![CDATA[Remote sensing-based detailed wetland classification: a review of advances from 2020 to 2025]]></title>
        <pubdate>2026-07-30T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ying He</author><author>Muzi Li</author><author>Xiaoyan Tang</author><author>Ping Ren</author>
        <description><![CDATA[Wetlands play an irreplaceable role in maintaining ecological balance and conserving biodiversity. However, driven by both natural and human factors, vast wetland areas worldwide are being rapidly converted into agricultural or urban land, leading to a sharp decline in their extent and a degradation of their quality. Against this backdrop, remote sensing technology, with its unique advantages in macro-level monitoring and multi-temporal dynamic capture, provides indispensable technical support for the precise identification, classification, and change detection of wetlands, making it an essential tool for wetland monitoring, restoration assessment, conservation planning, and SDG-related evaluation. This review examines recent advances in remote sensing for detailed wetland classification over the past 5 years. It elaborates on commonly used remote sensing data sources, local and international wetland classification standards, diverse classification methods, accuracy evaluation metrics, and prospects. This review provides a comprehensive reference to remote sensing-based wetland classification studies and applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1931394</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1931394</link>
        <title><![CDATA[Editorial: Machine learning for advanced remote sensing: from theory to applications and societal impact]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Rui Li</author><author>Shaoqing Dai</author><author>Bin Jiang</author><author>Cong Zhang</author><author>Haoyang Yang</author><author>Wufan Zhao</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1861186</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1861186</link>
        <title><![CDATA[High-precision individual tree canopy segmentation from UAV remote sensing imagery based on the improved BKFE-UNet model]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Qi Liu</author><author>Cui Jia</author><author>Linghan Gao</author><author>Haonan Wang</author><author>Chenfei Shi</author><author>Mengyu Xu</author><author>Kaiyu Jia</author><author>Siyu Ma</author>
        <description><![CDATA[Canopy segmentation is a crucial step in obtaining canopy parameters in forestry remote sensing, and it holds significant importance for research areas such as forest carbon sequestration. However, canopy segmentation based on UAV imagery still faces challenges including confusion between edges and background, as well as insufficient edge details. To address these issues, this paper proposes a semantic segmentation network based on U-Net which implements boundary and key feature enhancement named BKFE-UNet, which is built upon the U-Net model and integrates a differential boundary attention module (DBM) and a key feature enhancement module (KFEM). The DBM enhances canopy edge information through differential computation, alleviating problems such as adhesion between adjacent canopy edges and inadequate edge details. The KFEM introduces Ghost convolution; by stacking Ghost features, it simultaneously filters out redundant information and enhances the key semantic features of canopy objects, thereby reducing background interference. On the UAV tree canopy segmentation dataset, BKFE-UNet achieves an mPA of 92.62%, an mIoU of 86.50%, an Accuracy of 96.30%, and an F1-score of 0.90, representing improvements of 2.23%, 2.27%, 2.09%, and 0.02, respectively, over the baseline U-Net, demonstrating a significant improvement over the baseline U-Net model.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1827393</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1827393</link>
        <title><![CDATA[A multi-class, multi-temporal crop and Land cover mapping framework for Morocco using Sentinel-1/2 monthly composites and advanced machine learning ensembles]]></title>
        <pubdate>2026-07-21T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maryam Choukri</author><author>Yacine Bouroubi</author><author>Jamal-Eddine Ouzemou</author><author>Said Grich</author><author>Guy Armel Kamga Fotso</author><author>Saeid Ojaghi</author><author>Abdelghani Chehbouni</author><author>Ahmed Laamrani</author>
        <description><![CDATA[Timely and accurate crop-type mapping is fundamental for sustainable agricultural management and food security in semi-arid regions, where climate variability and fragmented landscapes present persistent challenges. This study develops and validates an operational, multi-temporal framework for classifying five key agricultural classes (i.e., soft wheat, durum wheat, barley, trees, and other crops) across diverse Moroccan agroecosystems. By integrating monthly Sentinel-1 Synthetic Aperture Radar and Sentinel-2 optical time series spanning six growing seasons (2018–2025), we extracted 156 features comprising 13 spectral indices across 12 monthly composites. Ground truth data from the national Al Moutmir database, strategically balanced to address natural class imbalances, supported comprehensive training and validation of six machine learning models (i.e., Random Forest, Extra Trees, XGBoost, LightGBM, Voting Ensemble, and Stacking Ensemble). Our findings showed that the LightGBM and Stacking Ensemble achieved the highest performance with 88.04% overall accuracy, followed closely by XGBoost (87.93%). Feature importance analysis revealed that monthly temporal resolution significantly outperformed traditional phenological-stage approaches, with March and April indices (particularly Normalized Difference Vegetation Index and Normalized Difference Red Edge) contributing most to class discrimination. Notably, early-season radar features (Vertical-Vertical polarization in September) provided valuable complementary information when optical data were limited. The framework demonstrated robust generalization through 10-fold cross-validation while explicitly quantifying a 12.56% overfitting gap (train-CV difference), acknowledging a non-negligible overfitting risk. Offering transparent performance assessment. Error analysis identified persistent confusion between spectrally similar cereals, particularly durum and soft wheat, highlighting priority areas for future sensor integration. This scalable, cloud-based pipeline directly supports Morocco’s Green Generation strategy by providing a reproducible, high-accuracy solution for annual crop inventories, with transferable applications across similar Mediterranean and semi-arid agricultural systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1882437</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1882437</link>
        <title><![CDATA[Neutrosophic soft-computing ensemble for high-accuracy satellite image scene classification]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ezhilmaran Devarasan</author><author>Maxime Mahieu Josse</author><author>Sm Arun</author><author>V. Vishal</author><author>Bapathu Koushika</author><author>Abhyuday Singh</author><author>Jenisha Rachel</author>
        <description><![CDATA[Satellite image scene classification is a fundamental task in remote sensing. It underpins land-use monitoring, urban planning, disaster response, and environmental management. Despite substantial progress through deep learning, complex aerial scenes remain challenging owing to high inter-class visual similarity, intra-class spatial variance, and inherent prediction uncertainty across heterogeneous model architectures. This paper proposes a novel triple-branch ensemble framework that combines three architecturally complementary deep learning backbones-ConvNeXt-Small, HRNet-W18, and Swin Transformer (Small) - to jointly exploit local texture hierarchies, high-resolution spatial representations, and global self-attention context. To advance beyond conventional probability averaging, three uncertainty-aware soft-computing fusion strategies are developed and compared: Normal Fuzzy logic, Intuitionistic Fuzzy logic, and Neutrosophic logic. The proposed Neutrosophic fusion decomposes each branch output into Truth, Indeterminacy, and Falsity components, explicitly down-weighting predictions characterised by high inter-branch disagreement. Experiments are conducted on the UC Merced Land Use Dataset (21 classes, 2,100 images) using a stratified 80/20 split with a comprehensive eight-stage preprocessing pipeline and ImageNet transfer learning. The proposed Neutrosophic Ensemble achieves 99.29% accuracy and the highest precision of 99.31%, outperforming all individual backbones and simpler ensemble baselines. These results suggest that architectural complementarity combined with neutrosophic uncertainty modelling is a promising approach for satellite image scene classification on this benchmark, though validation on larger and more diverse datasets remains an important next step.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1877713</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1877713</link>
        <title><![CDATA[Sequential feature selection for efficient landslide segmentation from multi-spectral data]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Arsalaan Ahmad</author><author>Oktay Karakuş</author><author>Paul L. Rosin</author>
        <description><![CDATA[Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic inputs whose contributions remain poorly understood. The question of which channels are actually necessary has received surprisingly little attention. This matters: redundant or correlated inputs obscure physical interpretability, inflate computational overhead, and can actively degrade model performance through the Hughes Phenomenon. We present a systematic, explainable channel-selection framework for the Landslide4Sense benchmark, combining Sentinel-2 multispectral and ALOS PALSAR terrain data with 16 engineered spectral and structural indices. Rather than relying on conventional single-band drop tests, which evaluate channels in isolation and miss interaction effects, we apply Sequential Forward Floating Selection (SFFS) to iteratively build and prune a candidate feature pool using a lightweight U-Net++ proxy model. Beyond identifying a compact 8-channel subset that matches or exceeds the segmentation F1 of configurations using up to 30 channels, we use the selection process itself to interrogate which spectral and topographic features landslide models genuinely rely on, and what this reveals about the physical cues driving their predictions. We argue that SFFS represents a principled feature selection approach to input design in Earth observation, in contrast to the prevailing practice of appending every available band and hoping the model learns what to ignore.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1869435</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1869435</link>
        <title><![CDATA[SOE-YOLO: towards efficient and accurate small object detection in optical remote sensing imagery]]></title>
        <pubdate>2026-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yimo Peng</author><author>Xiangyu Ge</author>
        <description><![CDATA[Small object detection in optical remote sensing imagery remains a formidable challenge due to severe background clutter, frequent missed detections, and the high computational overhead of existing models. Specifically, targets such as vehicles and ships occupy merely a few pixels, making their fine-grained details highly susceptible to degradation during network downsampling. To address these bottlenecks, we propose SOE-YOLO (Small Object Enhanced-YOLO11), a novel framework that strikes an optimal balance between accuracy and efficiency. We propose an Asymmetric Padding Down-Sampling (APDS) module to preserve spatial resolution, a C3k2-HFCE block to enhance high-frequency contrast, a Contextual Small-object Attention Fusion (CSAF) module and introduce a Dimension-Aware Selective Integration (DASI) module. Together, these components effectively bridge semantic gaps, adapt to multi-directional targets, and mitigate complex background interference. Extensive experiments demonstrate the superiority of SOE-YOLO. Compared to the baseline YOLO11n, our model achieves mAP50 improvements of 7.4%, 1.9%, and 3.7% on the VEDAI, RSOD, and NWPU VHR-10 datasets, reaching 67.8%, 90.8%, and 89.4%, respectively. Furthermore, SOE-YOLO maintains a lightweight profile with only 3.36 million parameters (corresponding to a model file size of 6.8 MB in FP32 precision and a computational cost of 10.8 GFLOPs, establishing a new state-of-the-art trade-off between detection robustness and computational efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1834812</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1834812</link>
        <title><![CDATA[Integrated spatial-contextual remote sensing classification via dual-path transformers and entropy-regularized HSIC]]></title>
        <pubdate>2026-06-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tallha Akram</author><author>Faheem Ul Rehman Siddiqi</author><author>Muqaddas Gull</author><author>Amal Al‐Rasheed</author><author>Muhammad Atif Imtiaz</author><author>Ali Hamdan Alenezi</author><author>Hira Siddique</author><author>Imran Ashraf</author>
        <description><![CDATA[IntroductionRemote sensing image classification is an important task in Earth observation. However, achieving high accuracy is still challenging. This is mainly due to high-dimensional feature redundancy, large intra-class variability, and the difficulty of capturing both fine spatial details and long-range contextual information. To address these challenges, this paper proposes a unified classification framework based on a novel Multi-Scale Dual-Path Shifted Pyramid Vision Transformer (M-DSPViT).MethodsThe proposed model improves standard Vision Transformer architectures by introducing dual-path shifted patch embedding and content-adaptive attention gating. It also incorporates multi-scale feature pyramid fusion, dynamic expert routing, and gradient-based attention masking to better capture spatial and contextual features. In addition, a hybrid feature selection method (HSIC-HFS) is introduced to remove redundant information and retain discriminative features. This method combines the Hilbert-Schmidt independence criterion, Shannon entropy, and L1-regularization. The refined features are then integrated with CNN-based spatial descriptors extracted using EfficientNet through a Sequential Feature Aggregation (SFA) framework.ResultsThe proposed method is evaluated on the WHU-RS19, UC Merced, and AID benchmark datasets. It achieves state-of-the-art performance in land-use classification. The robustness and generalization ability of the model are further validated through statistical analysis, including one-way ANOVA and F-statistic testing.DiscussionThe results confirm the stability of the proposed approach across different remote sensing scenarios.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1822070</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1822070</link>
        <title><![CDATA[Fine-grained crop classification from satellite image time series by boosting rare-class representations and enforcing global semantic consistency]]></title>
        <pubdate>2026-06-22T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Anqi Wang</author><author>Jiacheng Ge</author><author>Zhe Dong</author><author>Junchao Wu</author><author>Wei Guo</author>
        <description><![CDATA[Accurate and timely fine-grained crop type classification from satellite image time series is crucial for large-scale agricultural monitoring and decision support in food-security management. However, fine-grained classification remains challenging due to extreme class imbalance and high inter-crop spectral similarity, especially when rare crops occupy only small and fragmented parcels. We propose a rare-class-aware framework with global semantic consistency regularization for fine-grained crop classification from Sentinel-2 multispectral time series. Built on a spatiotemporal encoder–decoder backbone, the framework combines rare-class-aware patch sampling with spatiotemporal perturbations to strengthen minority-class representations, and a global semantic consistency regularization based on patch-level class proportion estimation to align patch composition with pixel-wise predictions. Experiments on the H2Crop benchmark (France, 2022–2023), which contains over 1 million annotated parcels and 101 fine-grained crop types, validate the proposed strategy. Our method achieves a mean F1-score of 36.72% and an IoU of 27.82%, consistently outperforming state-of-the-art approaches, with improvements of 4.38 percentage points in F1-score and 4.00 percentage points in IoU over the strongest competitor. These results demonstrate strong potential for reliable rare-crop recognition and fine-grained agricultural monitoring in large, highly imbalanced landscapes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1838521</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1838521</link>
        <title><![CDATA[A unified comparative framework for multiscale geometric transforms in SAR and multispectral satellite image analysis]]></title>
        <pubdate>2026-05-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sai Bhargav Kasetty</author><author>Rajakumar Krishnan</author>
        <description><![CDATA[Satellite image analysis is essential for remote sensing analysis. Two types of data are captured via satellite: Synthetic Aperture Radar (SAR) imagery (which has structure) and multispectral imagery (which contains spectral information), so complementary data may present unique challenges because noise, image resolution, and image angles differ across modalities. This paper describes the creation of a unified cross-modality framework to evaluate alternative transforms (Fourier, Wavelet, Curvelet, Shearlet, Contourlet) for satellite image analysis. The framework includes image processing for both SAR and multispectral modalities (image pre-processing, transform-based decomposition and reconstruction, image fusion, and multi-metric evaluation), all under one shared pipeline. Experimental results illustrate the performance of transforms on co-registered SAR and multispectral imagery. Results show that Fourier has produced poor results due to the loss of spatial information. Wavelet and Curvelet produced moderate results due to limited directional capabilities. Quality of reconstruction, edge preservation, and direction representation favored Shearlet over all other transforms. Contourlet produced strong results for both edge and texture modeling. Overall, this research reveals how well different types of geometric transforms, particularly Shearlet and Contourlet transforms, perform when processing satellite imagery.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1812755</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1812755</link>
        <title><![CDATA[S3ViT: self-supervised spectral vision transformer framework for hyperspectral unmixing]]></title>
        <pubdate>2026-04-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dario Scilla</author><author>Victor Angulo</author><author>Kasper Johansen</author><author>Naif Alsalem</author><author>Wolfgang Heidrich</author><author>Matthew F. McCabe</author>
        <description><![CDATA[Hyperspectral unmixing aims to decompose each pixel in a hyperspectral image into a set of constituent endmembers and their corresponding abundances. Recent deep learning based approaches have demonstrated strong performance in capturing both spectral and spatial features. However, obtaining reliable per-pixel abundance ground truth in real hyperspectral scenes is generally infeasible, which motivates unsupervised and self-supervised unmixing strategies. In this work, we propose S3ViT, a self-supervised Spectral Vision Transformer designed for pixel-wise hyperspectral unmixing. The transformer captures spectral and spatial dependencies by applying self-attention over the full sequence of pixel tokens (1×1) augmented with learnable positional embeddings. It operates without ground-truth annotations by generating pseudo labels through an unsupervised process: first, Singular Value Decomposition (SVD) is used to estimate the number of endmembers based on a thresholded singular value spectrum; then, k-means clustering provides cluster-derived priors that are used to form a contextual token and initialize spectral prototypes, without being treated as true abundance supervision. To guide training, two initialization tokens, one from Vertex Component Analysis (VCA) and one from the k-means cluster-derived priors, are embedded alongside patch tokens into the transformer. The model learns to estimate abundance maps while enforcing the Abundance Non-negativity and Sum-to-One constraints through ReLU and Softmax layers. Endmember spectra are later estimated from pixels with high predicted abundances. We evaluated S3ViT on the Samson, Jasper Ridge and Washington DC Mall benchmark datasets and compared it to state-of-the-art geometrical and deep learning methods. Our model achieves superior or comparable performance in both RMSE and SAD metrics, with up to 31% improvement in SAD and 25% in RMSE. These results indicate that a compact pixel-token ViT, guided by weak spectral priors and optimized via reconstruction losses, can achieve competitive unmixing performance on standard benchmarks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1765013</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1765013</link>
        <title><![CDATA[Benchmarking machine learning classifiers for urban mapping in arid environments: a google earth engine analysis of Riyadh’s expansion (1990–2025)]]></title>
        <pubdate>2026-03-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Amal Abdelsattar</author>
        <description><![CDATA[Monitoring urban expansion in arid regions is complicated by the spectral similarity between impervious surfaces and bare soil. Although machine learning classifiers on platforms like Google Earth Engine (GEE) offer effective solutions, their performance in these environments has not been systematically benchmarked. This study addresses this gap by comparing five supervised ML algorithms—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GBT), Classification and Regression Tree (CART), and k-Nearest Neighbor (KNN)—for binary urban and non-urban mapping. We applied this analysis to Riyadh, Saudi Arabia, using a 35-year Landsat time series from 1990 to 2025. Annual, radiometrically consistent median composites were generated in GEE, with the 2025 composite based on imagery from January to September 2025. A custom ten-band feature stack, including indices such as the Bare Soil Index (BSI), was used for classification. The Random Forest model (RF-100) achieved the highest accuracy (Overall Accuracy = 0.977, Kappa = 0.954) and was selected for final land-use and land-cover mapping. Validation with 600 independent samples per epoch and comparison to the ESA WorldCover 2020 dataset confirmed the robustness of the results. The analysis found a 293% increase in Riyadh’s built-up area, from 416 km2 in 1990 to 1,219 km2 in 2025, with a notable slowdown in growth after 2010. Variable importance analysis showed that the Bare Soil Index (BSI) and the Normalized Difference Built-up Index (NDBI) were the most significant features for class separation, offering key methodological insights for arid urban remote sensing. This work provides a transferable methodological framework for classifier and feature selection in arid environments and a high-accuracy spatiotemporal dataset establishing a baseline for assessing sustainable urban development under Saudi Vision 2030.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2026.1751006</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2026.1751006</link>
        <title><![CDATA[A fully satellite-driven workflow for hydrodynamic modeling in data-scarce coastal systems: integrating ICESat-2, Sentinel-2, SWOT and reanalysis models]]></title>
        <pubdate>2026-03-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ali Reza Payandeh</author><author>Marc Simard</author><author>Daniel Jensen</author><author>Anthony Daniel Campbell</author><author>Heidi van Deventer</author><author>Alexandra Christensen</author>
        <description><![CDATA[Hydrodynamic models in coastal and estuarine systems are typically constrained by sparse bathymetry, boundary, and validation data, especially in regions where field campaigns are costly or impractical. Here we develop and test a fully satellite-driven framework for hydrodynamic modeling in South Africa’s Langebaan Lagoon without using any local in situ measurements. Bathymetry is derived by training multispectral Sentinel-2 reflectance against ICESat-2 ATL24 photon-derived depths using an XGBoost model optimized with Bayesian search. The final satellite derived bathymetry reproduces independent ATL24 points with RMSE = 0.45 m and R2 = 0.97. This bathymetry was used in a depth-averaged Delft3D Flexible Mesh model driven at the open boundary by TPXO tidal harmonics and by ERA5 winds. We validate modeled water surface elevation against 16 SWOT low-rate (250 m, unsmoothed) passes in 2023. SWOT–model comparisons yield an overall RMSE of 0.11 m and R2 = 0.61, with typical point differences <0.10 m (∼5% of the 2 m tidal range), and showed consistent spatial gradients in water level from the offshore boundary, through Saldanha Bay, and into the lagoon. At the offshore boundary, TPXO and SWOT sea surface heights agree closely (R2 = 0.86). A ∼26 min phase lag, determined using a lag-correlation analysis, reduces the TPXO–SWOT RMSE from 0.18 m to 0.11 m, indicating that phase differences explain some of the mismatch, with remaining differences likely linked to non-tidal signals. Our results demonstrate that combining passive optical, photon-counting LiDAR, radar interferometry, and global tidal/atmospheric models enables robust, transferrable hydrodynamic modeling in data-scarce coastal systems, offering a cost-effective pathway for monitoring.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1731775</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1731775</link>
        <title><![CDATA[Research on automatic mosaicking and synthesis processing technology for multi-source remote sensing images]]></title>
        <pubdate>2026-01-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jing Cai</author><author>Feng Ye</author><author>Jingyu Sun</author><author>Hangan Wei</author><author>Zichuang Li</author><author>Pengao Li</author>
        <description><![CDATA[Multi - source remote sensing image automatic mosaic and synthesis processing technology is the key to improving the utilization efficiency of remote sensing data. With the rapid develop-ment of diversified imaging platforms such as satellites, unmanned aerial vehicles and ground sensors, the heterogeneity of image data sources has become increasingly prominent, which makes the difficulty of mosaic and synthesis increase. This paper focuses on the auto-matic mosaic and synthesis processing technology of multi - source remote sensing images. Firstly, an adaptive block - weighted Wallis parallel color equalization algorithm fusing specific scene constraints is designed. It dynamically adjusts the block size of color equalization pro-cessing through the coefficient of variation, and optimizes the calculation of local color param-eters combined with bilinear interpolation, which avoids the color distortion of traditional glob-al algorithms and significantly improves the efficiency of radiometric correction. Moreover, an adaptive mosaic algorithm is introduced, and a space - constrained Markov Random Field - Graph Cut seamline generation model is used to generate seamless synthetic images, which supports large - area coverage. This technology can be extended to environmental monitoring, disaster assessment and urban planning. It can automatically process massive multi - source da-ta and achieve high - precision synthesis.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1678991</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1678991</link>
        <title><![CDATA[Geological mapping of copper deposits in the democratic republic of Congo through remote sensing data and machine learning]]></title>
        <pubdate>2026-01-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matthieu Tshanga Matthieu</author><author>Lindani Ncube</author><author>Kgabo Humphrey Thamaga</author>
        <description><![CDATA[IntroductionThis study aimed to identify hydrothermal alteration zones and structural features associated with copper mineralisation in the Musonoi region, Lualaba Province, Democratic Republic of the Congo (DRC), within the Central African Copperbelt, using remote sensing and machine learning (ML). The study responds to the need for cost-effective and scalable exploration approaches in structurally complex tropical terrains.MethodsMultispectral satellite data from ASTER and Landsat 8 OLI, integrated with field observations and borehole information, supported the development of a predictive model. Principal Component Analysis (PCA), band ratios, and both manual and automated lineament extraction were used to enhance spectral and structural features. A lineament density map and hydrothermal alteration indices were produced and integrated with geological field data to verify the relationship between surface anomalies and subsurface mineralisation.ResultsRandom Forest classification indicated strong mineralisation in zones with high lineament density, fault intersections, and chlorite and kaolinite alteration. The main controlling variables were lineament density at 33.6%, fault proximity at 31.0%, and hydrothermal alteration index at 26.3%. Siliceous laminated rocks and basal dolomitic shale hosts mineralised units. Field validation confrmed that the model reflects known deposits, showing the strength of remote sensing and machine learning for exploration in complex tropical terrains.DiscussionThe study highlights the novelty of integrating Random Forest with multi source geospatial information in a structurally complex tropical terrain, and shows that this approach provides a cost effective and scalable tool for exploration in the Central African Copperbelt and similar geological provinces. Limitations related to spatial resolution and training data coverage remain and should be addressed in future work.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1718058</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1718058</link>
        <title><![CDATA[Efficient remote sensing image super-resolution with residual-enhanced wavelet and key-value adaptation]]></title>
        <pubdate>2026-01-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rongchang Lu</author><author>Hongbo Miao</author><author>Xin Hai</author>
        <description><![CDATA[Remote sensing image super-resolution (SR) is vital for urban planning, precision agriculture, and environmental monitoring, yet existing methods have limitations: CNNs with restricted receptive fields cause edge blurring, Transformers with O(L2d) complexity fail in gigapixel-scale processing, and SSMs (e.g., Mamba) have directional biases missing diagonal features. To address these issues, this study proposes the REW-KVA architecture, integrating three innovations: Residual-Enhanced Wavelet Decomposition for separating low/high-frequency features and suppressing noise; Linear Attention with Key-Value Adaptation (complexity O(Ld)) for global context modeling; and Quad-Directional Scanning for omnidirectional feature capture. Validated on five datasets (DFC 2019, OPTIMAL-31, RSI-CB, WHU-RS19, UCMD), REW-KVA achieves state-of-the-art PSNR (29.17 dB on DFC 2019, 31.08 dB on RSI-CB) and SSIM (0.8958 on DFC 2019, 0.9442 on RSI-CB). It reduces memory reads by 35%, parameters by 42% (vs. SwinIR), and processes 1024×1024 images in 0.47 s (3.2× faster than SwinIR), serving as a deployable solution for resource-constrained platforms.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1697897</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1697897</link>
        <title><![CDATA[Enhancing land cover classification in the heterogeneous landscape by integrating auxiliary data with Sentinel-2 imagery using the random forest algorithm]]></title>
        <pubdate>2026-01-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Irvin D. Shandu</author><author>Sifiso Xulu</author><author>Michael Gebreslasie</author>
        <description><![CDATA[Effective and accurate land use and land cover classification (LULC-C) is an indispensable exercise for various environmental management objectives, including past and future land-use dynamics, flood and runoff modelling. However, LULC-C is subject to several limitations, such as labour-intensive derivation of a labelled dataset. So, we aim to enhance LULC-C using auxiliary features: elevation, slope, aspect, distance from-(road, railway stations, rivers, water, and town), global human settlement built-up layer and remote sensing indices in the heterogeneous landscape of eThekwini (EM) and Nelson Mandela Bay Metropolitan (NMBM) using Sentinel-2 and random forests (RF). We compared two classification scenarios: (1) feature set including bands and indices, and (2) feature set including bands, indices, and auxiliary features. We trained and tested RF using block cross-validation and random hold-out (70/30) split and validated the classified image using independent validation and 30% subset, through overall accuracy (OA) and F1-score. The study quantified the uncertainty using a 95% confidence interval with bootstrapping samples of 1000 iterations, and quantify the significance of scenario 2 using McNemar and p-value. Pixel-wise quantity and allocation disagreement were derived to compare classification scenarios against the two 2020 reference maps for South African National Land Cover and Environmental System Research Institute. A class-by-class pixel comparison between classification scenarios underscores the potential of auxiliary features. While classification scenarios achieved comparable accuracy, scenario 2 superseded scenario 1 in all classification scheme. Using an independent validation, the study found confidence interval (CI) for OA of 83.63% CI: 77.78–88.89 improved to 89.47% CI: 84.79–94.15, respectively, for scenario 1 and scenario 2 over EM. Confirmed by NMBM, where OA of 82.29% CI: 76.57–87.43 stabilised to 88.57% CI: 84.00–93.14 for scenario 1 and scenario 2. The performance improvement was statistically significant, attaining p-values of 0.03 and 0.02, respectively, for EM and NMBM using an independent validation set. However, while using 30% validation subset, results show in-significant improvement in NMBM attaining p-value = 0.07, where p-value >0.05. Overall results proved that an integration of auxiliary features enhance LULC-C. The empirical result of this study is a step forward in effective LULC-C in a complex landscape.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1693286</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1693286</link>
        <title><![CDATA[Detection potential of floating matter in high-resolution X-band SAR data: initial results with visual interpretations]]></title>
        <pubdate>2025-12-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Madjid Hadjal</author><author>Brian B. Barnes</author><author>Chuanmin Hu</author><author>Lin Qi</author><author>Dimitris Papageorgiou</author><author>Konstantinos Topouzelis</author>
        <description><![CDATA[Remote detection of floating matter, such as macroalgae, plastics, or other debris, primarily relies on the use of passive optical imagery that requires daytime collection and an absence of clouds, sun glint, and thick aerosols. Synthetic aperture radar (SAR) sensors are not affected by these issues, but their capacity in such detection has not been robustly characterized. As such, this study qualitatively evaluates the capacity of Capella Space X-band (9.6 GHz) SAR, which provides data at a spatial resolution of 0.35–1 m, 100 to 800 times higher than what is currently available from Sentinel-1 C-band (5.4 GHz) SAR. A controlled experiment with floating plastic targets of 1 m2, 2 m2, and 3 m2 showed that only the 3 m2 target and 1 m2 mooring buoys were clearly detected and only in a single collection mode. Some macroalgae and floating plants, such as Ulva prolifera and hyacinth, were consistently detected by Capella SAR. However, Sargassum horneri and Sargassum natans/fluitans were only partially detected by Capella SAR, with larger aggregations providing more positive detections. Surface scums of phytoplankton such as Trichodesmium or Noctiluca were not detected. The main detection limitations arise from the weak contrast between the floating matter and the surrounding water, as well as the low signal-to-noise ratios (SNRs) of the three different collection modes of Capella SAR, which range from 2 to 6 (±0.03–0.35). On the other hand, Capella SAR successfully detected floating material in Lake Skadar/Shkodra (Albania and Montenegro) and foam and potential brine shrimp cysts in the Great Salt Lake, while these targets were not detected using Sentinel-1. Despite a limited dataset of only 33 SAR images paired with concurrent and co-located optical images, these preliminary results show the value of high-resolution X-band SAR in detecting relatively large mats of plastics and certain types of macroalgae. The findings can also help task the satellites to collect targeted images for event response.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frsen.2025.1678882</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frsen.2025.1678882</link>
        <title><![CDATA[A privacy-preserving, on-board satellite image classification technique incorporating homomorphic encryption and transfer learning]]></title>
        <pubdate>2025-12-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Abhijit Roy</author><author>Mahendra Kumar Gourisaria</author><author>Rajdeep Chatterjee</author><author>Amitkumar V. Jha</author><author>Bhargav Appasani</author><author>Nicu Bizon</author><author>Alin Gheorghita Mazare</author>
        <description><![CDATA[Satellite image classification is an important and challenging task in the modern technological age. Satellites can capture images of danger-prone areas with very little effort. However, the size and number of satellite images are very high when they are rapidly captured from space, and they require a huge amount of memory to store the data. In addition, keeping the satellite images private is another important task for security purposes. On-board, instant, accurate classification of a smaller number of satellite images is a challenging task, which is important to determine the specific condition of an area for instant monitoring. In the proposed hybrid approach, the captured images are kept secure, while the required training of the classification is done separately. Finally, the trained module is encrypted for use by the satellite to perform the on-board classification task. The Brakerski–Fan–Vercauteren (BFV)-based homomorphic encryption of EuroSAT satellite images is applied to store images in a cloud storage, where the privacy of the images can be maintained. Then, the decrypted images are used for training four transfer learning models (YOLOv8, YOLOv12, ResNet34, ResNet101, and a vision transformer classification. The best-trained module is encoded and encrypted again by using homomorphic encryption to limit the module to authorized devices. The encrypted module is decrypted and decoded to recover the trained module, which is used for instant classification of test images. Finally, the performance of the transfer learning models is evaluated from the test results. The vision transformer classifier achieved the highest accuracy of 99.65%.]]></description>
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