https://eoge.ut.ac.ir/ Earth Observation and Geomatics Engineering en daily 1 Wed, 01 Jul 2026 00:00:00 +0330 Wed, 01 Jul 2026 00:00:00 +0330 https://eoge.ut.ac.ir/article_107977.html Tropospheric phase delays are a major source of error in interferometric synthetic aperture radar (InSAR) measurements, particularly in coastal areas with complex atmospheric dynamics. Traditional correction methods often assume isotropy in atmospheric turbulence, overlooking potential directional dependencies. This study investigates the effectiveness of incorporating anisotropic models into tropospheric delay correction, focusing on geodynamically active coastal regions in southern Iran including the Makran coast, southern Zagros, and central Alborz as well as inland non-coastal areas such as South Khorasan. An empirical variogram analysis of over 1,200 interferograms revealed significant directional dependence in tropospheric turbulence, most evident in coastal zones where moisture distribution and topography are highly heterogeneous. Among the tested variogram models exponential, Gaussian, and spherical, the exponential model consistently yielded the best fit, reducing the root mean square error (RMSE) by up to 56.8%.Further evaluation using Least Squares Collocation (LSC) across four sampling scenarios demonstrated that anisotropic modeling significantly improves delay correction under sparse or irregular sampling conditions typical of coastal environments, achieving up to 15 cm improvement (50% RMSE reduction). In contrast, for densely and uniformly sampled regions, the gain was marginal (below 0.5 cm).These findings underscore the importance of accounting for anisotropic atmospheric behavior in InSAR applications. The proposed anisotropic correction framework offers practical value for improving deformation monitoring in regions with challenging atmospheric conditions. https://eoge.ut.ac.ir/article_107978.html This study proposes a fluid-dynamics–based approach for estimating the probable location of a sunken vessel. The last recorded position of the distressed ship from the Automatic Identification System (AIS) and environmental inputs—including wind direction, wind speed, and local bathymetric depth—are used as model drivers. A simplified Computational Fluid Dynamics (CFD) framework is adopted, in which the vessel is treated as a control volume subject to the primary forces of gravity, buoyancy, wave-induced drift, and the ship’s initial velocity. The results indicate that fusing the last-recorded AIS data with environmental data enables an initial estimate of the final resting position of the sunken hull. In addition, Virtual Probability Circles (VPCs) are constructed around the estimated position to represent uncertainty and to delineate a practical search area. The model was applied to the Verity shipwreck case in the North Sea, yielding an estimated wreck position at 54.0254°N, 7.6446°E. The computed total probable error was 229 m, corresponding to 50% and 95% probability circles with radii of 229 m and 458 m, respectively. These numerical results demonstrate the model’s practical capability for initial localization of sunken vessels and provide a decision-support tool for prioritizing search areas in maritime Search and Rescue (SAR) operations. https://eoge.ut.ac.ir/article_107979.html Accurate segmentation of orthoimages remains a significant challenge in remote sensing, particularly within complex urban environments and low-quality datasets. This study introduces PSO-SAM, a novel hybrid framework that enhances the Segment Anything Model (SAM) by automatically tuning key input parameters—namely points_per_side and pred_iou_thresh—using Particle Swarm Optimization (PSO). Evaluated on a 2013 low-resolution orthoimages of Yazd, Iran, PSO-SAM demonstrates substantial improvements over the baseline SAM in all major segmentation metrics: IoU (0.1882, +65.5%), Precision (0.2705, +17.8%), Dice Coefficient (0.3168, +55.2%), Recall (0.3821, +108%), and F1-Score (0.3168, +55.2%). Although the achieved absolute segmentation metrics remain modest due to the challenging characteristics of the dataset and zero-shot limitations of SAM, the proposed optimization framework consistently improves segmentation quality across all evaluation criteria, and absolute metrics are reported to provide balanced interpretation. The sharp rise in Recall highlights PSO-SAM’s capacity to reduce false negatives and detect geographic features more comprehensively. To the best of our knowledge, limited studies have explored the integration of SAM with metaheuristic optimization strategies for urban aerial image segmentation. While absolute metric values remain lower than state-of-the-art benchmarks—primarily due to data limitations and the sensitivity of PSO—the results emphasize the potential of PSO-SAM for automated segmentation in constrained remote sensing scenarios. Future directions include improving data quality, exploring advanced optimization strategies, and leveraging higher-resolution or multispectral datasets to close the performance gap. https://eoge.ut.ac.ir/article_107980.html Objective: This study presents the first comprehensive evaluation of pansharpening methods on Iran's Khayyam satellite imagery, launched in August 2022. Method: Twenty-one algorithms across traditional methods (including component substitution, multi-resolution analysis, and hybrid approaches) and advanced deep learning networks were compared using Khayyam data from Yazd-Iran, Semnan-Iran, and Baotou-China, with 0.8-meter panchromatic and 3.2-meter multispectral resolution. Traditional methods included 18 algorithms such as PCA, Gram-Schmidt variants, MTF-GLP methods, wavelet-based approaches, and hybrid combinations of wavelet and Gram-Schmidt techniques. Due to unique spectral characteristics, three deep learning networks (PNN, PanNet, PSGAN) were trained on Khayyam data. Quality assessment was conducted using visual analysis and quantitative metrics, including SAM, ERGAS, SSIM, PSNR, and UIQI for spectral quality, and SCC for spatial quality. Results: Results showed that traditional methods like GS-Wavelet and PRACS achieved superior performance with PSNR values exceeding 76 dB and SSIM above 0.99. Deep learning methods showed limited performance despite sensor-specific training, requiring extensive training data specific to Khayyam's sensor characteristics. Conclusions: No single method excelled universally, revealing trade-offs between spatial enhancement and spectral preservation. Baseline performance metrics for Khayyam imagery pansharpening were established, and insights for optimal method selection were provided. https://eoge.ut.ac.ir/article_107981.html Methane (CH₄), a potent greenhouse gas with a global warming potential far exceeding that of CO₂, is a critical driver of near-term climate change. Accurate identification and monitoring of high-emission point sources, also known as super-emitters, are essential for effective mitigation. Although multispectral satellite systems, such as Sentinel-2, provide the spatial resolution and coverage required for plume detection, conventional spectral ratio and enhancement techniques suffer from low sensitivity and high false favorable rates, often requiring manual verification. To address these limitations, we present a novel Dual-Stream Attention U-Net designed for automated methane plume segmentation from Sentinel 2 imagery. Our framework integrates two complementary input streams: (1) atmospherically corrected 12-band Sentinel 2 reflectance data, and (2) pre-computed Multi-Band Multi-Pass (MBMP) enhancement maps. The dual-encoder architecture learns modality-specific features that are fused at the bottleneck, while strategically placed attention gates, applied only to skip connections from the Sentinel-2 stream, dynamically suppress noise and emphasize plume-relevant regions. The model is trained and evaluated on the public CH4Net dataset using a composite Dice+BCE loss, and its robustness is enhanced by test-time augmentation and post-processing. Experimental results show substantial gains over the CH4Net baseline, achieving an IoU of 74.09%, Balanced Accuracy of 96.56%, and markedly lower false favorable rates. Qualitative analyses confirm the model’s ability to detect faint and irregular plumes in complex backgrounds where previous methods fail. These findings demonstrate that the proposed approach delivers scalable, high-precision, fully automated methane plume monitoring, suitable for operational deployment, and advancing global efforts in greenhouse gas mitigation. https://eoge.ut.ac.ir/article_107982.html Accurate monitoring of methane (CH₄) emissions from oil and gas infrastructure is critical for near-term climate change mitigation. While hyperspectral satellites like PRISMA provide high-fidelity data, the optimal deep learning architecture for segmenting faint methane plumes remains undetermined. This study provides the first systematic comparison of convolutional and transformer-based models for this task.We evaluate four architectures: an attention-enhanced U-Net++, Segformer, and two novel hybrids – SwinV2-UPP (SwinV2 encoder + U-Net++ decoder) and SwinV2-Former (SwinV2 encoder + MLP decoder). Models are trained on 73 manually delineated methane plumes from 34 PRISMA scenes across global oil and gas basins, augmented to 1,446 patches.SwinV2-UPP achieved the highest F1-score of 0.834, a 4.0% improvement over the convolutional baseline (0.802). Notably, SwinV2-Former achieved state-of-the-art precision of 0.910 – outperforming SwinV2-UPP by 2.5% – demonstrating exceptional false-positive minimization. In zero-shot generalization, SwinV2-UPP maintained the highest F1 (0.725), while SwinV2-Former achieved the best precision (0.857).We reveal a critical architectural trade-off: SwinV2-UPP balances precision and recall (F1=0.834) for comprehensive plume mapping, whereas SwinV2-Former prioritizes high-confidence detection (precision=0.910) for regulatory applications where false positives are costly. These findings provide a framework for selecting optimal deep learning architectures based on specific methane monitoring objectives. https://eoge.ut.ac.ir/article_107983.html Article type: Research ArticleArticle history: Received Received in revised form Accepted Available online Keywords: Low-cost GNSS receiver, Precise Point Positioning (PPP),IGS products, Urban canyon, Multipath mitigation. Objective: The recent evolution of low-cost dual-frequency Global Navigation Satellite System (GNSS) receivers has created new opportunities for high-precision positioning in cost-sensitive applications. However, limited studies have systematically evaluated their Precise Point Positioning (PPP) performance under varying environmental conditions and processing configurations. This study assesses the feasibility and positioning performance of a low-cost dual-frequency GNSS receiver in semi-obstructed and open-sky environments using PPP and Standard Point Positioning (SPP) techniques.Method: Static GNSS observations were collected with and without a manufacturer-provided ground plane to evaluate its effectiveness in mitigating multipath effects. PPP solutions were processed using ultra-rapid, rapid, and final International GNSS Service (IGS) orbit and clock products, and performance was compared across different environmental conditions.Results: Results indicate that environmental condition is the dominant factor influencing positioning performance. The use of a ground plane reduced multipath errors, particularly in obstructed environments. In open-sky conditions, PPP achieved centimeter to sub-decimetre level accuracy, especially when using rapid or final IGS products, whereas SPP remained limited to meter-level accuracy. Final products provided the highest positioning precision, while rapid products offered a practical balance between accuracy and convergence time.Conclusions: The results demonstrate that low-cost GNSS receivers can support reliable high-precision positioning in professional and mass-market applications when supported by appropriate processing strategies and hardware configurations. https://eoge.ut.ac.ir/article_107984.html Floods rank among the most destructive natural disasters worldwide, with their frequency and severity escalating due to climate change, rapid urbanization, and land-use changes. Accurate and timely flood detection is critical for effective disaster response and mitigation, especially in vulnerable arid and semi-arid regions frequently affected by flash floods. This study develops a multi-stage fusion framework for flood mapping by integrating Sentinel-1 SAR and Sentinel-2 multispectral data within the Google Earth Engine (GEE) cloud platform. The proposed approach was applied to the extreme flash flood event that struck Konarak County, southeastern Iran, in early January 2022. In the first stage, five supervised machine learning classifiers, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Tree (CART), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were independently trained using the fused SAR and optical dataset. In the next stage, Initial flood maps generated by the classifiers were fused at the decision level using the majority voting approach to produce the final flood map. The results show that the decision-level fusion achieved superior performance in terms of statistical metrics. It yielded an overall accuracy of 97.50%, F1-score of 97.42%, and a Kappa coefficient of 0.939, outperforming all individual classifiers considered in this study. In contrast, the RF classifier, which demonstrated the best performance among the classifiers, achieved an overall accuracy of 96.67%, an F1-score of 96.61%, and a Kappa coefficient of 0.933. The proposed method jointly exploits SAR and optical features and combines multiple classifier outputs to improve the accuracy and reliability of flood detection. https://eoge.ut.ac.ir/article_107985.html Synthetic Aperture Radar (SAR) plays a vital role in remote sensing due to its ability to capture high-resolution images regardless of weather conditions or daylight. However, to transform the raw SAR signals into interpretable imagery, advanced data processing techniques are essential. A widely used technique for this purpose is the Range Doppler Algorithm (RDA), which takes advantage of Fast Fourier Transform (FFT) to convert signals into the frequency domain for further processing. However, the computational cost of this approach becomes significant when dealing with large datasets. This paper presents a Quantum Range Doppler Algorithm (QRDA) that utilizes the Quantum Fourier Transform (QFT) to offer a theoretical exponential processing speedup of up to N/(log N)^2 compared to the classical FFT. However, realizing this end-to-end acceleration in practice requires overcoming significant bottlenecks related to classical data amplitude encoding and quantum measurement overheads. Furthermore, it introduces a quantum implementation of the Range Cell Migration Correction (RCMC) in the Fourier domain, a critical step in the RDA pipeline that realigns the received echoes so that the energy from a target is concentrated in a single range bin across all azimuth positions. The performance of the quantum RCMC is evaluated and compared against its classical counterpart, as an isolated operation, and then as part of the full classical pipeline. https://eoge.ut.ac.ir/article_107986.html Rapid and reliable landslide detection in densely vegetated mountainous regions remains challenging, particularly under persistent cloud cover and adverse weather conditions where optical imagery becomes ineffective. This study proposes an integrated PolSAR and PolInSAR framework based on freely available Sentinel-1 dual-polarization SAR data for landslide detection following the 2024 Enga Province landslide event in Papua New Guinea. Polarimetric features, including entropy, alpha angle, radar vegetation index, cross-polarization ratio, and covariance matrix elements, were extracted to characterize changes in scattering mechanisms and vegetation structure. In addition, multi-temporal coherence features were generated from pre-event, post-event, and cross-event image pairs to capture decorrelation associated with surface displacement and structural changes. To evaluate the contribution of different feature groups, coherence-based, covariance-based, PolSAR, and integrated feature stacks were classified using machine learning approaches. Among the evaluated classifiers, random forest achieved the best overall performance with an overall accuracy of 0.88. Quantitative evaluation revealed that while the coherence-only and covariance-based stacks yielded accuracies of 0.63 and 0.82 respectively, the PolSAR feature stack provided strong discriminative capability with an accuracy of 0.87. The integration of polarimetric and interferometric features further improved classification performance, with the full feature stack achieving the highest accuracy (0.88) and F1-score of 0.88. These results demonstrate the robustness and effectiveness of utilizing dual-polarimetric Sentinel-1 data for rapid and accurate landslide mapping in complex, cloud-prone environments. https://eoge.ut.ac.ir/article_107987.html Objective: This study develops a route-based spatial modeling framework to investigate travel time inequity (TTI) in access to urban parks across Tehran, compares alternative spatial regression models, and introduces a diagnostic Commercial Intensity Index (CII) for interpreting model performance under different land-use conditions.Method: TTI was defined as the normalized average travel time from each Traffic Analysis Zone (TAZ) to its nearest park using segment-level speeds derived from real-time traffic data. Area Ratio (AR) and Count-Area Ratio (CAR) indicators were calculated for residential, commercial, and educational land uses along shortest travel paths. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale GWR (MGWR) models were compared. A Commercial Intensity Index (CII) was also developed as a diagnostic indicator to support the interpretation of spatial model performance under different land-use configurations.Results: OLS achieved an adjusted R² of 0.904 but showed strong residual spatial autocorrelation and systematic overestimation. GWR improved local model fit (mean R² = 0.972) and reduced residual clustering, while MGWR produced the best overall performance (R² = 0.974; AICc = –171.0) by accounting for variable-specific spatial scales. Residential AR is generally associated with lower TTI, whereas commercial AR exhibited spatially heterogeneous effects. Within the analyzed dataset, CII-based clustering identified three spatial regimes—peripheral, transitional, and core zones—that were associated with the optimal performance of OLS, MGWR, and GWR, respectively.Conclusions: The results highlight the importance of accounting for spatial heterogeneity and land-use intensity in modeling park accessibility inequities. The study contributes a route-based land-use modeling framework together with a Commercial Intensity Index that facilitates the interpretation of spatial regression model performance under different urban land-use configurations. https://eoge.ut.ac.ir/article_107988.html Managing rapid urbanization requires reliable tools to forecast how metropolitan areas expand, especially in semi-arid regions where seasonal vegetation changes and reflective barren soils often confuse traditional models. We address this by developing a hybrid simulation framework that couples deep learning with spatial modeling to project urban growth in the Mashhad metropolitan area, Iran, up to 2030. Utilizing seasonal Sentinel-2 time series alongside Google's Dynamic World land-cover dataset, we trained a U-shaped Spatio-Temporal Attention (U-TAE) network to capture multi-seasonal spectral features. This step generated high-fidelity built-up probability maps, where a decision threshold of 0.20 was chosen via sensitivity analysis to optimize spatial accuracy. These maps then served as the transition potential layer within a Cellular Automata (CA) model. During training, the U-TAE model converged smoothly, reaching a validation Intersection over Union (IoU) of 0.9836 at epoch 11. Crucially, the spatiotemporal attention mechanism successfully distinguished highly reflective peripheral soils from permanent structures, eliminating seasonal false positives that typically impair static baselines. The integrated U-TAE-CA model effectively simulated low-density sprawl and edge expansion along dynamic corridors. These results demonstrate that combining temporal deep learning attention with cellular simulation provides a robust, realistic approach to monitoring dynamic land-use patterns, offering planners actionable predictive insights for sustainable infrastructure development. https://eoge.ut.ac.ir/article_106730.html Drought is a natural hazard that affects multiple aspects of human life and poses serious threats to ecosystems. In recent decades, the frequency and intensity of droughts have increased worldwide. Remote sensing has proven to be a valuable tool for monitoring drought and its impacts. This study investigates drought propagation (DP) from meteorological drought (MD) to agricultural drought (AD), as well as the seasonal factors influencing it, in Khuzestan Province, Iran, during the period 2000–2022. The Standardized Precipitation Index (SPI) and the Standardized Soil Moisture Index (SMI) were applied at 1-, 3-, and 12-month timescales to identify MD and AD. Drought events were detected using the Run Theory method, and the Maximum Correlation Coefficient (MCC) method was employed to estimate propagation times from MD to AD. Influencing factors, including precipitation, soil moisture, and vegetation (NDVI), were also examined. The results revealed that MD and AD occurred during 2008–2009, 2016–2019, and 2021–2022. Moreover, the analysis indicated that from 2000 to 2022, the frequency, duration, and intensity of drought events have increased, showing a clear upward trend over time. Propagation times were relatively short in spring, summer, and autumn (1–3 months), but were longest in winter (up to 7 months). The MCC values for summer, spring, autumn, and winter were 0.79, 0.63, 0.75, and 0.66, respectively. Precipitation had the greatest influence on AD in spring (r= 0.58) and autumn (r= 0.37), while vegetation played a more significant role in spring (r= 0.51). A strong correlation among soil moisture, precipitation, and NDVI during spring indicated an active hydrological cycle and rapid vegetative response, which accelerated drought propagation. These findings provide insights into seasonally vulnerable periods and can support the development of more effective drought early warning systems. https://eoge.ut.ac.ir/article_106731.html Urban flooding poses a significant and growing threat to densely populated areas, creating an urgent need for accurate and rapid mapping tools to support disaster response and mitigation efforts. This study presents a comparative evaluation of object-based and pixel-based classification approaches for urban flood detection using Sentinel-1 Synthetic Aperture Radar (SAR) data. The proposed methodology incorporates key SAR-derived features, including backscatter-based indices, the Urban Flood Index (UFI), interferometric coherence, and texture metrics extracted using the Gray Level Co-occurrence Matrix (GLCM), to enhance flood mapping accuracy. This study also introduces an object-based multi-feature framework that integrates these SAR-derived indicators to improve urban flood delineation. Both approaches utilized Random Forest classifiers trained with high-resolution optical imagery as reference data. In the object-based method, multi-resolution segmentation was applied to group homogeneous image objects and extract aggregated feature values within each segment. This approach produced flood probability maps that closely aligned with visually interpreted reference data, accurately delineating inundated urban zones in Golestan Province, Iran. Conversely, the pixel-based method preserved fine spatial detail but showed increased sensitivity to speckle noise and misclassification, particularly in heterogeneous built-up areas. A quantitative assessment demonstrated that the object-based approach outperformed the pixel-based method, achieving an overall classification accuracy of 97.06% compared to 83.09%. These results highlight the value of incorporating spatial context and region-based analysis for effective flood detection. The integration of SAR-derived backscatter, coherence, and texture features within an object-based framework proves to be a reliable strategy for operational urban flood mapping.