Egyptian Journal of Remote Sensing and Space Sciences最新文献_第2页

The Pacific Ocean decadal satellite data analysis and ENSO events connectivity to the Halmahera Sea 太平洋年代际卫星数据分析和ENSO事件与Halmahera海的连通性

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-01-18 DOI: 10.1016/j.ejrs.2026.01.006

Muhammad Zainuddin Lubis , Andrean V.H. Simanjuntak , Batara , Ekoué Ewane Blaise Arnold , Song Hu

The Pacific Ocean (PO) greatly influences the Indonesian sea, plays a crucial role in global climate and ocean circulation, and connects to the Indian Ocean (IO). Previous research has examined the Pacific Ocean’s influence, but the roles of eddies and primary productivity in marine ecosystems during the ENSO response within the Indonesian Throughflow (ITF) in the Halmahera Sea (HS) remain unclear. This study utilizes satellite data to analyze the spatiotemporal patterns of various oceanographic parameters with the ITF during ENSO events from 2009 to 2020. The analysis incorporates satellite-derived SST, SSH, SWH, Chl-a, zooplankton variability, and volume transport. Analysis of SST and SSH variability shows the influence of SWH. The EOF analysis reveals that SSH and SST variability are impacted by ENSO events, with notable fluctuations observed in 2010–2011 and 2015–2016. The EKE divergence is higher during La Niña than El Niño events. The volume transport is strongest at depths of 100–150 m during El Niño and 50–100 m during La Niña, with interannual variations ranging from −0.709 ± 1.394 Sv in the 0–50 m layer, decreasing with depth. Our findings show different patterns for Chl-a, SST, and SSS during the ENSO events.

太平洋（PO）对印尼海的影响很大，在全球气候和海洋环流中起着至关重要的作用，并与印度洋（IO）相连。以前的研究已经检查了太平洋的影响，但是在哈马黑拉海（HS）印度尼西亚通流（ITF）内的ENSO响应期间，漩涡和初级生产力在海洋生态系统中的作用仍然不清楚。利用卫星资料，利用ITF分析了2009 - 2020年ENSO事件期间各海洋参数的时空格局。该分析结合了卫星获得的海表温度、海面温度、海面温度、Chl-a、浮游动物变异性和体积运输。海表温度和海面高度的变率分析显示了SWH的影响。EOF分析表明，海表温度和海面温度的变率受到ENSO事件的影响，在2010-2011年和2015-2016年出现了显著波动。ela Niña事件的EKE散度高于El Niño事件。在El Niño和La Niña期间，体积输送在100 ~ 150 m深度最强，在0 ~ 50 m层的年际变化范围为- 0.709±1.394 Sv，随深度减小。我们的研究结果表明，在ENSO事件期间，Chl-a、SST和SSS具有不同的模式。

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引用次数: 0

Optimal feature-based InSAR phase filtering framework using convolutional neural network and mathematical morphology 基于卷积神经网络和数学形态学的InSAR相位滤波优化框架

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-01-16 DOI: 10.1016/j.ejrs.2026.01.001

Mahmoud S. Hamid , Mohamed Ghetas , Khaled M. Reda , M. Safy

Denoising interferometric phase data is a critical processing step for precise topographic reconstruction and land deformation analysis. A noisy interferogram leads to phase unwrapping challenges and may ultimately degrade the quality of various InSAR final products. In this paper, an optimal feature-based interferometric phase image filtering framework is proposed. The filtering process begins by extracting a set of features from simulated interferogram phase image that captures the primary structural elements of the interferometric phase data. Then, a set of soft morphological filters is optimized using the interferogram features set in the complex number field. A convolutional neural network is developed and employed to match patches of real interferogram phase images with the set of interferogram phase image features. The interferogram phase image patches are then filtered with their corresponding optimal filters. The convolutional neural network is trained using a dataset of interferogram phase image features with various noise variance. The proposed framework considers the effect of interferogram noise level on filtering performance by optimizing the filter parameters according to the level of the interferometric phase noise. Experimental results on simulated and real interferogram phase images confirm that the proposed interferogram filtering framework achieves efficient interferogram phase unwrapping as it contributed to high equivalent number of looks (ENL) of 5921 and a minimal number of phase residues (NOR) of 123. The results also approve that the proposed approach has perfect preservation of fringe pattern and interferogram fine detail compared to other state of the art approaches.

干涉相位数据去噪是精确地形重建和陆地变形分析的关键处理步骤。噪声干涉图会导致相位展开挑战，并可能最终降低各种InSAR最终产品的质量。本文提出了一种基于特征的干涉相位图像滤波优化框架。滤波过程首先从模拟干涉图相位图像中提取一组特征，该图像捕获了干涉相位数据的主要结构元素。然后，利用复数域的干涉图特征集对一组软形态滤波器进行优化。提出了一种卷积神经网络，将实际干涉图相位图像的块与干涉图相位图像特征集进行匹配。然后用相应的最优滤波器对干涉图相位图像块进行滤波。卷积神经网络是利用具有不同噪声方差的干涉图相位图像特征数据集进行训练的。该框架考虑了干涉图噪声水平对滤波性能的影响，根据干涉相位噪声水平对滤波器参数进行优化。仿真和真实干涉图相位图像的实验结果表明，所提出的干涉图滤波框架实现了高效的干涉图相位展开，其等效外观数（ENL）为5921，相位残留数（NOR）为123。结果还表明，与现有的方法相比，该方法具有较好的条纹图案和干涉图细节的保存。

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引用次数: 0

AN MLPEL machine learning model for bathymetry retrieval based on ensemble learning 基于集成学习的MLPEL测深检索机器学习模型

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2026-01-14 DOI: 10.1016/j.ejrs.2026.01.003

Jinshan Zhu , Cong Jiao , Ruifu Wang , Yuquan Wen , Yu Wang , Bopeng Liu , Yina Han

Machine learning models have made rapid progress in recent years. As a feedforward neural network, the multilayer perceptron (MLP) is widely used in bathymetry because of its simple structure and good nonlinear fitting ability. However, the classical multilayer perceptron method is faced with a potential problem, which is easy to fall into local minima and overfitting, resulting in failure of model training. In order to solve the problems of overfitting and weak ability to capture complex environments of a single model, this paper propose a multilayer perceptron as the meta-model Ensemble Learning Model (MLPEL) In this model, CatBoost, MLP and Random Forest (RF) are selected for the base model to make preliminary prediction and generate new dataset. MLP model is selected as the meta-model to receive new dataset and predict water depth. To validate the feasibility of the model, we conducted experiments in two study areas, Ganquan Island and Vieques. In Ganquan island, the MSE of MLPEL model is 1.30 m², which is 0.6–2.4 m² lower than that of Log-Ratio (LR) model and MLP model. In Vieques island, the MSE of the MLPEL model is 1.54 m², which is 0.5–1.5 m² lower than the LR model and the MLP model. The results show that this method can solve the local minimum problem of multi-layer perceptron model well, and obtain the water depth value with high robustness and accuracy.

近年来，机器学习模型取得了迅速的进展。多层感知器（MLP）作为一种前馈神经网络，以其结构简单、非线性拟合能力好等优点在水深测量中得到广泛应用。然而，经典的多层感知器方法面临着一个潜在的问题，即容易陷入局部极小和过拟合，导致模型训练失败。为了解决单一模型的过拟合和捕获复杂环境能力弱的问题，本文提出了一种多层感知器作为元模型集成学习模型（MLPEL），该模型选择CatBoost、MLP和Random Forest （RF）作为基础模型进行初步预测并生成新的数据集。选择MLP模型作为元模型接收新数据集并进行水深预测。为了验证模型的可行性，我们在甘泉岛和别克斯岛两个研究区域进行了实验。在甘泉岛，MLPEL模型的MSE为1.30 m2，比Log-Ratio （LR）模型和MLP模型的MSE低0.6 ~ 2.4 m2。在别克斯岛，MLPEL模型的MSE为1.54 m2，比LR模型和MLP模型低0.5-1.5 m2。结果表明，该方法能较好地解决多层感知器模型的局部最小值问题，获得的水深值具有较高的鲁棒性和精度。

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引用次数: 0

Integration between spatial analysis with MCDA for best site selection of hospitals: A case study of Port Said Governorate, Egypt 将空间分析与MCDA相结合用于医院最佳选址：以埃及塞得港省为例

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-11 DOI: 10.1016/j.ejrs.2025.11.005

Hisham Nabil, Mahmoud El-Mewafi, Mohamed Zhran

The recognition and prioritization of hospital locations plays an important role in shaping the healthcare framework of any nation. The process of selecting suitable sites is an intricate, multi-faceted decision-making endeavor involving various stakeholders with their unique interests. The main objective of this study is to ascertain the most appropriate approach for the selection of the best location for the new hospital. Multi-criteria decision analysis (MCDA) presents a promising solution due to the multitude of criteria integral to the decision-making process. Accordingly, the current research focuses on four criteria (population density, proximity to road, distance to existing hospitals, and slope). This study employs three MCDA techniques, namely, analytical hierarchy process (AHP), fuzzy AHP (FAHP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Additionally, MCDA techniques utilizing geographical information systems (GIS), applied to Port Said Governorate, Egypt, are introduced. The results obtained through the application of AHP, FAHP, and TOPSIS are carefully compared, encompassing criterion rankings and suggested hospital site locations. These results indicate that AHP, FAHP, and TOPSIS give the same result for the best site selection for the new hospital in the study area. The results of this study provide decision-makers and urban planners vital information that makes it easier to pinpoint the best site location for a new hospital.

医院位置的识别和优先排序在塑造任何国家的医疗保健框架方面都起着重要作用。选择合适地点的过程是一个复杂的、多方面的决策过程，涉及不同利益相关者的独特利益。本研究的主要目的是确定为新医院选择最佳位置的最合适方法。多准则决策分析（MCDA）提出了一个有前途的解决方案，由于众多的标准组成的决策过程。因此，目前的研究重点是四个标准（人口密度、靠近道路、与现有医院的距离和坡度）。本研究采用三种MCDA技术，即分析层次分析法（AHP）、模糊层次分析法（FAHP）和理想解相似偏好排序法（TOPSIS）。此外，还介绍了在埃及塞得港省应用的利用地理信息系统（GIS）的MCDA技术。通过应用AHP、FAHP和TOPSIS获得的结果进行了仔细的比较，包括标准排名和建议的医院选址。这些结果表明，AHP、FAHP和TOPSIS对研究区新医院的最佳选址给出了相同的结果。这项研究的结果为决策者和城市规划者提供了重要的信息，使他们更容易确定新医院的最佳选址。

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引用次数: 0

Comparative assessment of GIS-based multi-criteria decision analysis (AHP) and machine learning (MaxEnt) approaches for wildfire susceptibility modeling in Montenegro 基于gis的多准则决策分析（AHP）和机器学习（MaxEnt）方法在黑山野火易感性建模中的比较评估

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 DOI: 10.1016/j.ejrs.2025.11.003

Filip Vujović , Goran Grozdanić , Radovan Đurović , Aleksandar Valjarević , Ivica Milevski

This study provides a national-scale assessment of wildfire susceptibility in Montenegro by comparing two modeling frameworks: a Geographic Information System–based Multi-Criteria Decision Analysis (GIS-MCDA) using fuzzy standardization with the Analytic Hierarchy Process (AHP), and a machine-learning approach based on the Maximum Entropy model (MaxEnt). Historical wildfire occurrences from the Moderate Resolution Imaging Spectroradiometer (MODIS) archive (2001–2024) were analyzed together with initial thirteen geoenvironmental initial causal criteria spanning vegetation, climatic, topographic and anthropogenic factors. After the Variance Inflation Factor (VIF) analysis, eleven causal criteria were retained for further modeling. Results of validation using receiver operating characteristic (ROC) curves showed clear performance differences: GIS-MCDA with fuzzy standardization and AHP weighting achieved low predictive accuracy (area under the curve, AUC = 0.51), whereas MaxEnt performed strongly (AUC = 0.81). These findings highlight the ability of MaxEnt to capture nonlinear relationships and complex interactions among geoenvironmental causal criteria, whereas GIS-MCDA with fuzzy standardization and AHP proved inadequate for reliable wildfire susceptibility assessment in this context. These results confirm the findings of previous studies, while showing an even weaker performance of GIS-MCDA with fuzzy standardization and AHP compared to earlier studies. Importantly, this is the first application of machine learning through MaxEnt for wildfire susceptibility assessment in Montenegro, providing a spatial basis for wildfire management and a foundation for national-scale wildfire risk assessment.

本研究通过比较两种建模框架：基于地理信息系统的多标准决策分析（GIS-MCDA）和基于最大熵模型（MaxEnt）的机器学习方法，对黑山的野火易感性进行了全国范围的评估。利用2001-2024年MODIS档案资料，结合植被、气候、地形和人为因素等13个地质环境初始因果准则，对历史野火发生情况进行了分析。在方差膨胀因子（VIF）分析后，保留了11个因果标准以进一步建模。使用受试者工作特征（ROC）曲线进行验证的结果显示出明显的性能差异：采用模糊标准化和AHP加权的GIS-MCDA的预测精度较低（曲线下面积，AUC = 0.51），而MaxEnt的预测精度较高（AUC = 0.81）。这些发现强调了MaxEnt能够捕捉地质环境因果标准之间的非线性关系和复杂相互作用，而具有模糊标准化和层次分析法的GIS-MCDA在这种情况下不足以进行可靠的野火易感性评估。这些结果证实了之前的研究结果，但与之前的研究相比，模糊标准化和层次分析法的GIS-MCDA的表现更弱。重要的是，这是通过MaxEnt首次将机器学习应用于黑山的野火易感性评估，为野火管理提供了空间基础，为全国范围的野火风险评估奠定了基础。

{"title":"Comparative assessment of GIS-based multi-criteria decision analysis (AHP) and machine learning (MaxEnt) approaches for wildfire susceptibility modeling in Montenegro","authors":"Filip Vujović , Goran Grozdanić , Radovan Đurović , Aleksandar Valjarević , Ivica Milevski","doi":"10.1016/j.ejrs.2025.11.003","DOIUrl":"10.1016/j.ejrs.2025.11.003","url":null,"abstract":"<div><div>This study provides a national-scale assessment of wildfire susceptibility in Montenegro by comparing two modeling frameworks: a Geographic Information System–based Multi-Criteria Decision Analysis (GIS-MCDA) using fuzzy standardization with the Analytic Hierarchy Process (AHP), and a machine-learning approach based on the Maximum Entropy model (MaxEnt). Historical wildfire occurrences from the Moderate Resolution Imaging Spectroradiometer (MODIS) archive (2001–2024) were analyzed together with initial thirteen geoenvironmental initial causal criteria spanning vegetation, climatic, topographic and anthropogenic factors. After the Variance Inflation Factor (VIF) analysis, eleven causal criteria were retained for further modeling. Results of validation using receiver operating characteristic (ROC) curves showed clear performance differences: GIS-MCDA with fuzzy standardization and AHP weighting achieved low predictive accuracy (area under the curve, AUC = 0.51), whereas MaxEnt performed strongly (AUC = 0.81). These findings highlight the ability of MaxEnt to capture nonlinear relationships and complex interactions among geoenvironmental causal criteria, whereas GIS-MCDA with fuzzy standardization and AHP proved inadequate for reliable wildfire susceptibility assessment in this context. These results confirm the findings of previous studies, while showing an even weaker performance of GIS-MCDA with fuzzy standardization and AHP compared to earlier studies. Importantly, this is the first application of machine learning through MaxEnt for wildfire susceptibility assessment in Montenegro, providing a spatial basis for wildfire management and a foundation for national-scale wildfire risk assessment.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 4","pages":"Pages 724-736"},"PeriodicalIF":4.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal glacier velocity variations and their controlling factors in the Nathorstbreen glacier system, Svalbard 斯瓦尔巴群岛Nathorstbreen冰川系统冰川速度变化及其控制因素

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-12-01 DOI: 10.1016/j.ejrs.2025.11.004

Supratim Guha , Hyun-Cheol Kim

Temporal variations in glacier velocity are not only essential to understand glacier dynamics but also to predict glacier hazards. Therefore, in the current study, the continuous glacier velocities were estimated from 2014 to 2023 in the Nathorstbreen Glacier System (NGS), Svalbard, where a recent surge event has been observed. Also, the study identified and quantified the factors controlling variations of annual glacier velocity.

Using Landsat 8 OLI images, Cossi-corr (Co-registration of Optically Sensed Images and Correlation), an advanced Fourier-based image-matching tool, was utilized to estimate the velocity of the NGS. A multivariate regression analysis was performed to evaluate the influence of temperature, precipitation, snowfall, and terminus fluctuations on annual velocity changes.

The results indicate that the NGS exhibited the highest and lowest average annual velocities in 2021 and 2018, with magnitudes of 0.86 ± 0.11 m/day and 0.34 ± 0.18 m/day, respectively. The lower velocity in 2018 represents a quiescent phase following the previous surge, whereas the acceleration in 2021 reflects renewed dynamic activity linked to terminus retreat. Overall, glacier velocity declined from 2014 to 2018, increased between 2020 and 2022, and slightly decreased again in 2023. During this period, the glacier terminus experienced alternating annual retreat and advance, resulting in a net retreat of approximately 2.9 km. Terminus fluctuations were identified as important factors influencing annual glacier velocity, showing a lagged response between terminus movement and velocity. Including parameters such as ice thickness and subglacial hydrology in future analyses would further improve understanding of glacier velocity controls.

冰川流速的时间变化不仅是了解冰川动态的必要条件，而且也是预测冰川灾害的必要条件。因此，在本研究中，估计了2014年至2023年斯瓦尔巴群岛Nathorstbreen冰川系统（NGS）的连续冰川速度，在那里观测到最近的涌流事件。此外，研究还确定并量化了控制冰川年速度变化的因素。利用Landsat 8 OLI图像，利用基于傅里叶的先进图像匹配工具cossicorr （Co-registration of optical sensing images and Correlation）来估计NGS的速度。采用多元回归分析，评价了气温、降水、降雪量和终端线波动对年速度变化的影响。结果表明，NGS的年平均速度在2021年和2018年最高和最低，震级分别为0.86±0.11 m/d和0.34±0.18 m/d。2018年的较低速度代表了之前激增之后的静止阶段，而2021年的加速反映了与终端撤退相关的新的动态活动。总体而言，冰川速度在2014年至2018年期间下降，在2020年至2022年期间增加，并在2023年再次略有下降。在此期间，冰川末端经历了每年交替后退和前进的过程，导致净后退约2.9公里。终端波动是影响冰川年速度的重要因素，终端运动与速度之间存在滞后响应。在未来的分析中包括冰层厚度和冰下水文等参数将进一步提高对冰川速度控制的理解。

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引用次数: 0

Sentinel-2 and Planet-Scope as reliable tools for water quality monitoring of small reservoirs Sentinel-2和Planet-Scope是小型水库水质监测的可靠工具

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-11-17 DOI: 10.1016/j.ejrs.2025.11.002

Silvia Di Francesco , Francesco Biondi , Barbara Casentini , Stefano Fazi , Stefano Amalfitano , Marco D’Eugenio , Francesca Todisco , Stefano Casadei , Francesca Giannone

In an era marked by increasingly frequent extreme weather events, small inland reservoirs are emerging as crucial yet often overlooked water resources. This study investigates the potential of remote sensing techniques to efficiently monitor the water quality of those reservoirs and improve their management. Although many works in literature have tried to derive water quality parameters from different satellite platforms, micro satellites constellations like PlanetScope have never been investigated: they can be a promising tool for investigation of SRs thanks to their high spatial and temporal resolution. Focusing on Spina Reservoir, a small lake in the province of Perugia where a water quality survey has been conducted, the research combines on-site biochemical analyses with satellite imagery from Sentinel-2, well known and explored free Platform, and PlanetScope. The performances of images corrected with the default atmospheric correction and with a specific pre-processor (ACOLITE) for inland and coastal water are discussed.

Water Quality Semi-empirical algorithms (indices) based on one or more spectral bands at different wavelengths are used to build correlation curves respect to in-situ measurements (e.g Chlorophyll-a, turbidity, Cyanobacteria), enabling the evaluation and comparison of the performance.

PlanetScope images displayed higher reliability with respect to Sentinel-2 data and correction with ACOLITE lead to more accurate interpolations, except for chlorophyll-a, even if satellite images with lower spatial resolution (Sentinel-2) can also provide a well-distributed dataset.

The findings underscore the significant potential of PlanetScope microsatellite constellation for real-time, cost-effective water quality assessment that could be easily applied on a larger scale, as regional assessment.

在一个极端天气事件日益频繁的时代，小型内陆水库正成为至关重要但往往被忽视的水资源。本研究探讨了遥感技术在有效监测水库水质和改善水库管理方面的潜力。尽管文献中的许多作品都试图从不同的卫星平台上获得水质参数，但像PlanetScope这样的微型卫星星座从未被研究过：由于它们具有高时空分辨率，它们可以成为研究SRs的有前途的工具。该研究以佩鲁贾省的斯皮纳水库（Spina Reservoir）为研究对象，将现场生化分析与Sentinel-2卫星图像结合起来，Sentinel-2是著名的免费平台，PlanetScope也进行了水质调查。讨论了使用默认大气校正和特定预处理（ACOLITE）对内陆和沿海水域进行校正的图像的性能。基于不同波长的一个或多个光谱波段的半经验算法（指数）用于建立与原位测量（例如叶绿素-a，浊度，蓝藻）相关的曲线，从而对性能进行评估和比较。PlanetScope图像相对于Sentinel-2数据显示出更高的可靠性，即使空间分辨率较低的卫星图像（Sentinel-2）也可以提供分布良好的数据集，但ACOLITE校正导致更准确的插值（叶绿素-a除外）。这些发现强调了PlanetScope微卫星星座在实时、具有成本效益的水质评估方面的巨大潜力，可以很容易地应用于更大规模的区域评估。

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引用次数: 0

Iron ore exploration in the Central Eastern Desert of Egypt: Insights from remote Sensing, Geophysical, and geochemical data 埃及中东部沙漠的铁矿勘探：来自遥感、地球物理和地球化学数据的见解

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-11-11 DOI: 10.1016/j.ejrs.2025.11.001

Mahmoud Abd El-Rahman Hegab , Salem Mohamed Salem , Nehal Mohamed Soliman , Kareem Hamed Abd El Wahid , Soha Hassan , Alaa Nayef , Mohamed Anwar Ahmed

The novelty of this study lies in applying an integrated workflow that combines geological mapping, aeromagnetic analysis, remote sensing, and XRF analysis to delineate extensions of known iron ore deposits and identify previously unrecognized occurrences, while simultaneously providing new insights into the tectono-magmatic controls of iron mineralization in the Central Eastern Desert. The findings provide critical data on the spatial organization, mineral characteristics, and geological controls of iron ore in this complex tectonic setting, enabling more efficient exploration plans in the Eastern Desert. Metamorphosed banded iron formations (BIFs) prevail at several localities, e.g., Gabal El Hadid, Umm Nar, Umm Ghamis El Zarqa, El Sibai, El Dabbah, and Wadi Kareem. These BIFs occur within a metavolcano-sedimentary environment, with thicknesses of up to 5 m, in the form of bands and lenses composed of magnetite, hematite, and silica. Magnetic spectral analysis enabled clear discrimination among lithological units, definition of structural controls, and demarcation of alteration zones associated with iron mineralization.

这项研究的新颖之处在于，它将地质测绘、航磁分析、遥感和XRF分析相结合，应用了一个集成的工作流程，以描绘已知铁矿床的延伸，并识别以前未被识别的矿点，同时为中东部沙漠中铁矿化的构造-岩浆控制提供了新的见解。这一发现为研究这一复杂构造环境下的铁矿石空间组织、矿物特征和地质控制提供了关键数据，有助于制定更有效的东部沙漠勘探计划。变质带状铁地层（BIFs）在Gabal El Hadid、Umm Nar、Umm Ghamis El Zarqa、El Sibai、El Dabbah和Wadi Kareem等几个地方普遍存在。这些bif出现在变质火山-沉积环境中，厚度可达5米，以磁铁矿、赤铁矿和二氧化硅组成的带状和透镜状的形式存在。通过磁谱分析，可以清晰地区分岩性单元，明确构造控制，划分与铁矿成矿有关的蚀变带。

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引用次数: 0

Advancing coastal land use mapping through deep multi-label classification and multi-sensor data fusion 通过深度多标签分类和多传感器数据融合推进沿海土地利用制图

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-10-24 DOI: 10.1016/j.ejrs.2025.10.004

Alireza Sharifi , Mohammad Mahdi Safari , Bayan Alabdullah

Coastal environments change and are environmentally sensitive. Land use classification must be accurate and timely for sustainable development, environmental monitoring, and catastrophe risk management. This research introduces a deep learning framework for categorizing coastal land use with multiple labels using high-resolution satellite pictures from several sensors. We design and evaluate a deep convolutional neural network architecture that classifies photos with multiple labels optimally using the MLRSNet dataset, which comprises 60 semantic classes from Chinese coastal locations. Data fusion merges spectral, spatial, and textural characteristics from many remote sensing methods, making classification findings more trustworthy and relevant to more circumstances. Numerous studies have proven that our method accurately separates complex and visually similar coastal categories including wetlands, beaches, rivers, ships, and urban coastlines. Precision, recall, F1-score, and mAP are used to evaluate the model. We also analyze its performance and mistakes in each class. The results demonstrate how deep learning and data fusion may address coastal remote sensing issues such semantic ambiguity, class variability, and class imbalance. This study enhances geographic artificial intelligence (GeoAI) by showing how to create a high-resolution shoreline map using a framework that works from start to end, can be scaled up, and can be utilized elsewhere. The recommended strategy affects environmental monitoring, coastal zone management, and fact-based decision-making, notably with climate change and urbanization along the coastline. Deep learning and multi-sensor satellite technologies can improve operational coastal monitoring systems, according to our findings.

沿海环境不断变化，对环境十分敏感。土地利用分类必须准确、及时地用于可持续发展、环境监测和巨灾风险管理。本研究引入了一个深度学习框架，利用来自多个传感器的高分辨率卫星图片，对沿海土地使用进行多标签分类。我们设计并评估了一个深度卷积神经网络架构，该架构使用MLRSNet数据集对具有多个标签的照片进行最佳分类，该数据集包含来自中国沿海地区的60个语义类。数据融合融合了许多遥感方法的光谱、空间和纹理特征，使分类结果更加可信，适用于更多的情况。许多研究已经证明，我们的方法可以准确地分离复杂的和视觉上相似的海岸类别，包括湿地、海滩、河流、船舶和城市海岸线。使用Precision， recall， F1-score和mAP来评估模型。我们还分析了它在每节课上的表现和错误。研究结果表明，深度学习和数据融合可以解决沿海遥感问题，如语义模糊、类别可变性和类别不平衡。这项研究通过展示如何使用从头到尾工作的框架创建高分辨率海岸线地图来增强地理人工智能（GeoAI），该框架可以按比例放大，并可以在其他地方使用。建议的策略影响环境监测、海岸带管理和基于事实的决策，特别是沿海地区的气候变化和城市化。根据我们的研究结果，深度学习和多传感器卫星技术可以改善沿海监测系统的运行。

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引用次数: 0

Deep multimodal unmixing of hyperspectral images using Convolutional Block Attention Module (CBAM) and LiDAR features 基于卷积块注意模块（CBAM）和激光雷达特征的高光谱图像深度多模态解混

IF 4.1 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-10-21 DOI: 10.1016/j.ejrs.2025.10.002

M Sreejam, L Agilandeeswari

Hyperspectral image unmixing has garnered considerable attention across various application domains, particularly remote sensing applications. However, relying solely on one modality to distinguish objects with similar spectral information presents several shortcomings. Enhanced performance can be achieved by integrating geographical information from Light Detection and Ranging (LiDAR) data into Unmixing. This paper introduces a new unmixing model that combines hyperspectral and LiDAR data. Impressive data representation and feature extraction using deep learning technology have been employed to develop the Multimodal Hyperspectral Unmixing Model using CBAM (Convolutional Block Attention Module) attention (MHUCBAM). The model exemplifies a sophisticated approach to multimodal unmixing, incorporating Spectral Spatial attention alongside the CBAM. Channel Attention improved the model’s capability to analyze complex spatial and spectral relationships. Our model achieves accurate unmixing of complex environments with effective multimodal data representation and deep feature extraction. Two real-world multimodal unmixing datasets, namely, Houston and Muffle, are used for the performance evaluation. A rigorous ablation analysis was performed to validate the performance of the proposed model. The comparative study with existing unmixing models demonstrated that utilizing latent features from LiDAR data resulted in better unmixing outcomes in terms of both Root Mean Square Error (RMSE) and Spectral Angular Distance (SAD).

高光谱图像解混已经在各个应用领域引起了相当大的关注，特别是遥感应用。然而，仅仅依靠一种模态来区分具有相似光谱信息的物体存在一些缺点。通过将来自光探测和测距（LiDAR）数据的地理信息集成到Unmixing中，可以提高性能。本文介绍了一种结合高光谱和激光雷达数据的新解混模型。使用深度学习技术的令人印象深刻的数据表示和特征提取被用于开发使用CBAM（卷积块注意模块）注意（MHUCBAM）的多模态高光谱解混模型。该模型体现了一种复杂的多模态解混方法，将频谱空间关注与CBAM结合在一起。通道注意提高了模型分析复杂空间和光谱关系的能力。该模型通过有效的多模态数据表示和深度特征提取实现了复杂环境的精确解混。两个真实世界的多模态解混数据集，即Houston和Muffle，用于性能评估。进行了严格的烧蚀分析，以验证所提出模型的性能。与现有解混模型的对比研究表明，利用LiDAR数据的潜在特征在均方根误差（RMSE）和光谱角距离（SAD）方面都可以获得更好的解混结果。

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