In light of issues such as unnoticeable texture features and limited resolution of infrared image objects, a lightweight multi-scale feature fusion method for UAV infrared object recognition is presented to enhance the performance of UAVs carrying intelligent devices to detect infrared objects. By changing the anchorless frame strategy of the YOLOX method, a lightweight Multi-Feature Fusion Network (MFFNet) for UAV IR image object recognition is proposed. First, a lightweight backbone network is built using ShuffleNetv2_block, spatial pyramid pooling, and other modules to reduce the network's number of parameters and inference time while maintaining its capacity to extract features. Second, we develop a multi-feature fusion module to improve the detection capabilities of the model for IR objects by fusing the local features and the overall characteristics of IR objects since the texture features of IR objects are challenging to employ, but the boundary information is evident. The boundary frame regression loss is then optimized using SIoU by comparing the predicted frame to the actual frame in terms of angle, distance, shape, and IoU (Intersection over Union), which forces the model to reach the optimum predicted box more quickly.
针对红外图像物体纹理特征不明显、分辨率有限等问题,提出了一种用于无人机红外物体识别的轻量级多尺度特征融合方法,以提高搭载智能设备的无人机探测红外物体的性能。通过改变 YOLOX 方法的无锚帧策略,提出了一种用于无人机红外图像物体识别的轻量级多特征融合网络(MFFNet)。首先,利用 ShuffleNetv2_block、空间金字塔池化等模块构建了轻量级骨干网络,在保持特征提取能力的同时减少了网络的参数数量和推理时间。其次,我们开发了一个多特征融合模块,通过融合红外物体的局部特征和整体特征来提高模型对红外物体的检测能力,因为红外物体的纹理特征很难利用,但边界信息却很明显。然后利用 SIoU 对边界框回归损失进行优化,将预测框与实际框在角度、距离、形状和 IoU(Intersection over Union)方面进行比较,从而迫使模型更快地达到最佳预测框。
{"title":"MFFNet: A lightweight multi-feature fusion network for UAV infrared object detection","authors":"Yunlei Chen , Ziyan Liu , Lihui Zhang , Yingyu Wu , Qian Zhang , Xuhui Zheng","doi":"10.1016/j.ejrs.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.001","url":null,"abstract":"<div><p>In light of issues such as unnoticeable texture features and limited resolution of infrared image objects, a lightweight multi-scale feature fusion method for UAV infrared object recognition is presented to enhance the performance of UAVs carrying intelligent devices to detect infrared objects. By changing the anchorless frame strategy of the YOLOX method, a lightweight Multi-Feature Fusion Network (MFFNet) for UAV IR image object recognition is proposed. First, a lightweight backbone network is built using ShuffleNetv2_block, spatial pyramid pooling, and other modules to reduce the network's number of parameters and inference time while maintaining its capacity to extract features. Second, we develop a multi-feature fusion module to improve the detection capabilities of the model for IR objects by fusing the local features and the overall characteristics of IR objects since the texture features of IR objects are challenging to employ, but the boundary information is evident. The boundary frame regression loss is then optimized using SIoU by comparing the predicted frame to the actual frame in terms of angle, distance, shape, and IoU (Intersection over Union), which forces the model to reach the optimum predicted box more quickly.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 268-276"},"PeriodicalIF":6.4,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000218/pdfft?md5=85d30684c98bfb92e8845e2acca9c06c&pid=1-s2.0-S1110982324000218-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140327714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-25DOI: 10.1016/j.ejrs.2024.03.004
Ali Asghar Alesheikh , Zahra Chatrsimab , Fatemeh Rezaie , Saro Lee , Ali Jafari , Mahdi Panahi
Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.
{"title":"Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach","authors":"Ali Asghar Alesheikh , Zahra Chatrsimab , Fatemeh Rezaie , Saro Lee , Ali Jafari , Mahdi Panahi","doi":"10.1016/j.ejrs.2024.03.004","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.004","url":null,"abstract":"<div><p>Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 255-267"},"PeriodicalIF":6.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000243/pdfft?md5=716d865dbcbf1efa7542c8800ffe7a5d&pid=1-s2.0-S1110982324000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140290314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-21DOI: 10.1016/j.ejrs.2024.03.002
Shiva Aghapour Maleki, Hassan Ghassemian, Maryam Imani
Pansharpening involves the fusion of panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution image with enhanced spectral and spatial information. Assessing the quality of the resulting fused image poses a challenge due to the absence of a high-resolution reference image. Numerous methods have been proposed to address this, from assessing quality at reduced resolution to full-resolution evaluations. Many existing approaches are pixel-based, where quality metrics are applied and averaged on individual pixels. In this article, we introduce a novel object-based method for assessing the quality of pansharpened images at full resolution. In object-based quality assessment methods, the reaction of different areas of the fused image to the fusion process is reflected. Our approach revolves around extracting objects from the given image and evaluating extracted objects. By doing so, the distinct responses of different objects within the fused image to the fusion process are captured. The proposed method leverages a unique object extraction technique known as segmentation by nearest neighbor (SNN) to extract objects of the MS image. This method extracts the objects based on the image’s characteristics without any requirement for parameter tuning. These extracted objects are then mapped onto both PAN and fused images. The proposed spectral index measures the spectral homogeneity of the fused image’s objects and the proposed spatial index measures the injected spatial content from the PAN image to the fused image’s objects. Experimental results underscore the robustness and reliability of the proposed method. Additionally, by visualizing distortion values on object-maps, we gain insights into fusion quality across diverse areas within the scene.
全色锐化是将全色(PAN)和多光谱(MS)图像进行融合,以获得具有增强光谱和空间信息的高分辨率图像。由于缺乏高分辨率参考图像,评估融合图像的质量成为一项挑战。为了解决这个问题,人们提出了许多方法,从评估降低分辨率的质量到评估全分辨率的质量。许多现有方法都是基于像素的,即在单个像素上应用质量指标并求取平均值。在本文中,我们将介绍一种基于对象的新方法,用于评估全分辨率平锐图像的质量。在基于对象的质量评估方法中,融合图像的不同区域对融合过程的反应得到了反映。我们的方法主要是从给定图像中提取对象,并对提取的对象进行评估。通过这种方法,可以捕捉到融合图像中不同对象对融合过程的不同反应。所提出的方法利用一种称为 "近邻分割"(SNN)的独特对象提取技术来提取 MS 图像中的对象。该方法根据图像的特征提取对象,无需调整参数。然后将这些提取的对象映射到 PAN 和融合图像上。所提出的光谱指数衡量融合图像对象的光谱同质性,所提出的空间指数衡量从 PAN 图像到融合图像对象的注入空间内容。实验结果凸显了所提方法的鲁棒性和可靠性。此外,通过可视化对象地图上的失真值,我们可以深入了解场景中不同区域的融合质量。
{"title":"Nonreference object-based pansharpening quality assessment","authors":"Shiva Aghapour Maleki, Hassan Ghassemian, Maryam Imani","doi":"10.1016/j.ejrs.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.002","url":null,"abstract":"<div><p>Pansharpening involves the fusion of panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution image with enhanced spectral and spatial information. Assessing the quality of the resulting fused image poses a challenge due to the absence of a high-resolution reference image. Numerous methods have been proposed to address this, from assessing quality at reduced resolution to full-resolution evaluations. Many existing approaches are pixel-based, where quality metrics are applied and averaged on individual pixels. In this article, we introduce a novel object-based method for assessing the quality of pansharpened images at full resolution. In object-based quality assessment methods, the reaction of different areas of the fused image to the fusion process is reflected. Our approach revolves around extracting objects from the given image and evaluating extracted objects. By doing so, the distinct responses of different objects within the fused image to the fusion process are captured. The proposed method leverages a unique object extraction technique known as segmentation by nearest neighbor (SNN) to extract objects of the MS image. This method extracts the objects based on the image’s characteristics without any requirement for parameter tuning. These extracted objects are then mapped onto both PAN and fused images. The proposed spectral index measures the spectral homogeneity of the fused image’s objects and the proposed spatial index measures the injected spatial content from the PAN image to the fused image’s objects. Experimental results underscore the robustness and reliability of the proposed method. Additionally, by visualizing distortion values on object-maps, we gain insights into fusion quality across diverse areas within the scene.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 227-241"},"PeriodicalIF":6.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232400022X/pdfft?md5=7dc512ed1d8a885a84a80f360ca1e4a9&pid=1-s2.0-S111098232400022X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-21DOI: 10.1016/j.ejrs.2024.02.007
Dipanwita Haldar , E. Suriya , Abhishek Danodia , R.P. Singh
The morphological shape and structure of the crop vary with phenological stages. Model and eigen based decomposition model parameters extracted from the Radarsat-2 data and the trend with respect to ground truth crop phenology were analysed. Sensitive parameters were devised through stepwise approach under 7 combinations of polarimetric variables of increasing complexity were assessed. Compared under the three machine learning algorithms (ANN, RF and SVM) where ANN rendered the maximum correlation with 0.92 with a MAE of 4 days which was implemented on a large parcel of maize mask in the study area. SVM performed poorly with highly overlapping parameters such as backscatter but performed well (r = 0.85). For assessing the crop biophysical parameters, the three algorithms were evaluated and sensitivity analysis for statistically significant polarimetric variables for biophysical parameters was performed. The assessment was performed on Multi-Layer Perception (MLP) neural network. The networks were trained with algorithms and hidden layer nodes until the MAE achieved permissible limits. Plant height could be estimated more profoundly with an r = 0.8 with a considerably good MAE of 24.9 cm but other parameters (WB, DB and LAI) were estimated in moderate correlation of 0.6–0.65 where the MAE of WB, DB and LAI were found to be 1317gm−2, 553 gm−2 and 0.78 respectively. This is the first step towards understanding the complex scattering mechanisms in Indian maize scenario assessing the growth parameters from polarimetric data. Thus, the analytical findings brought out possess the potential to serve as the reference for the future research initiatives.
{"title":"Morphological characterization of Maize (Zea mays.) utilising the stage-wise structural and architectural perspective from temporal fully-polarimetric SAR","authors":"Dipanwita Haldar , E. Suriya , Abhishek Danodia , R.P. Singh","doi":"10.1016/j.ejrs.2024.02.007","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.02.007","url":null,"abstract":"<div><p>The morphological shape and structure of the crop vary with phenological stages. Model and eigen based decomposition model parameters extracted from the Radarsat-2 data and the trend with respect to ground truth crop phenology were analysed. Sensitive parameters were devised through stepwise approach under 7 combinations of polarimetric variables of increasing complexity were assessed. Compared under the three machine learning algorithms (ANN, RF and SVM) where ANN rendered the maximum correlation with 0.92 with a MAE of 4 days which was implemented on a large parcel of maize mask in the study area. SVM performed poorly with highly overlapping parameters such as backscatter but performed well (r = 0.85). For assessing the crop biophysical parameters, the three algorithms were evaluated and sensitivity analysis for statistically significant polarimetric variables for biophysical parameters was performed. The assessment was performed on Multi-Layer Perception (MLP) neural network. The networks were trained with algorithms and hidden layer nodes until the MAE achieved permissible limits. Plant height could be estimated more profoundly with an r = 0.8 with a considerably good MAE of 24.9 cm but other parameters (WB, DB and LAI) were estimated in moderate correlation of 0.6–0.65 where the MAE of WB, DB and LAI were found to be 1317gm<sup>−2</sup>, 553 gm<sup>−2</sup> and 0.78 respectively. This is the first step towards understanding the complex scattering mechanisms in Indian maize scenario assessing the growth parameters from polarimetric data. Thus, the analytical findings brought out possess the potential to serve as the reference for the future research initiatives.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 242-254"},"PeriodicalIF":6.4,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000139/pdfft?md5=ab45c7b042e521d22619b44a72ce9fd4&pid=1-s2.0-S1110982324000139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-11DOI: 10.1016/j.ejrs.2024.03.003
Zeeshan Zafar , Muhammad Zubair , Yuanyuan Zha , Shah Fahd , Adeel Ahmad Nadeem
The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.
{"title":"Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data","authors":"Zeeshan Zafar , Muhammad Zubair , Yuanyuan Zha , Shah Fahd , Adeel Ahmad Nadeem","doi":"10.1016/j.ejrs.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.03.003","url":null,"abstract":"<div><p>The rapid increase in population accelerates the rate of change of Land use/Land cover (LULC) in various parts of the world. This phenomenon caused a huge strain for natural resources. Hence, continues monitoring of LULC changes gained a significant importance for management of natural resources and assessing the climate change impacts. Recently, application of machine learning algorithms on RS (remote sensing) data for rapid and accurate mapping of LULC gained significant importance due to growing need of LULC estimation for ecosystem services, natural resource management and environmental management. Hence, it is crucial to access and compare the performance of different machine learning classifiers for accurate mapping of LULC. The primary objective of this study was to compare the performance of CART (Classification and Regression Tree), RF (Random Forest) and SVM (Support Vector Machine) for LULC estimation by processing RS data on Google Earth Engine (GEE). In total four classes of LULC (Water Bodies, Vegetation Cover, Urban Land and Barren Land) for city of Lahore were extracted using satellite images from Landsat-7, Landsat-8 and Landsat-9 for years 2008, 2015 and 2022, respectively. According to results, RF is the best performing classifier with maximum overall accuracy of 95.2% and highest Kappa coefficient value of 0.87, SVM achieved maximum accuracy of 89.8% with highest Kappa of 0.84 and CART showed maximum overall accuracy of 89.7% with Kappa value of 0.79. Results from this study can give assistance for decision makers, planners and RS experts to choose a suitable machine learning algorithm for LULC classification in an unplanned urbanized city like Lahore.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 216-226"},"PeriodicalIF":6.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000231/pdfft?md5=248a24bc9935c1a4646bb7ace2188f1d&pid=1-s2.0-S1110982324000231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140103632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-07DOI: 10.1016/j.ejrs.2024.02.008
Ahmed Abdalla , Siavash Shami , Mohammad Amin Shahriari , Mahdi Khoshlahjeh Azar
Subsidence in southeastern Louisiana is a significant geological issue caused by natural and human-induced factors like low-lying topography and groundwater pumping. Human activities also led to coastal land loss and reduced sediment supply. Satellite-based technologies such as Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are used to monitor subsidence. Louisiana has about 130 continuously operating reference stations (CORS) monitoring subsidence statewide. GNSS provides accurate point measurements but limited spatial coverage. InSAR, however, detects ground deformation over large areas using satellite-based radar imagery. In response to this advantage, we employed Sentinel-1 SAR images from 2017 to 2021 to estimate the vertical displacement in East Baton Rouge (EBR) Parish. Significant displacement is found in urban and industrial areas, particularly in high- and medium-density residential areas. The significant subsidence area is between Denham Spring and Baton Rouge faults, where residential areas experience displacement of -0.7 to -1 cm/year. The displacement variation in land use indicates significant annual subsidence in some buildings and infrastructure. Three strategic facilities in Baton Rouge Downtown experienced displacement, with -6.1 mm/yr in Downtown, -2.99 mm/yr at Horace Wilkinson Bridge, and -4.94 mm/yr at central railway station. In addition, machine learning is employed to estimate the vertical displacement in the study area. The K-Nearest Neighbors (KNN) model provides a comprehensive understanding of subsidence estimation among the GBR (Gradient Boosting Regression), RFR (Random Forest Regression), and KNN models. Machine learning models revealed that proximity to fault lines and precipitation are the most influential factors in displacement.
{"title":"Estimation of land displacement in East Baton Rouge Parish, Louisiana, using InSAR: Comparisons with GNSS and machine learning models","authors":"Ahmed Abdalla , Siavash Shami , Mohammad Amin Shahriari , Mahdi Khoshlahjeh Azar","doi":"10.1016/j.ejrs.2024.02.008","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.02.008","url":null,"abstract":"<div><p>Subsidence in southeastern Louisiana is a significant geological issue caused by natural and human-induced factors like low-lying topography and groundwater pumping. Human activities also led to coastal land loss and reduced sediment supply. Satellite-based technologies such as Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are used to monitor subsidence. Louisiana has about 130 continuously operating reference stations (CORS) monitoring subsidence statewide. GNSS provides accurate point measurements but limited spatial coverage. InSAR, however, detects ground deformation over large areas using satellite-based radar imagery. In response to this advantage, we employed Sentinel-1 SAR images from 2017 to 2021 to estimate the vertical displacement in East Baton Rouge (EBR) Parish. Significant displacement is found in urban and industrial areas, particularly in high- and medium-density residential areas. The significant subsidence area is between Denham Spring and Baton Rouge faults, where residential areas experience displacement of -0.7 to -1 cm/year. The displacement variation in land use indicates significant annual subsidence in some buildings and infrastructure. Three strategic facilities in Baton Rouge Downtown experienced displacement, with -6.1 mm/yr in Downtown, -2.99 mm/yr at Horace Wilkinson Bridge, and -4.94 mm/yr at central railway station. In addition, machine learning is employed to estimate the vertical displacement in the study area. The K-Nearest Neighbors (KNN) model provides a comprehensive understanding of subsidence estimation among the GBR (Gradient Boosting Regression), RFR (Random Forest Regression), and KNN models. Machine learning models revealed that proximity to fault lines and precipitation are the most influential factors in displacement.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 204-215"},"PeriodicalIF":6.4,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000206/pdfft?md5=91c12c68bd62cf089fd1ea755786956f&pid=1-s2.0-S1110982324000206-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140062540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-27DOI: 10.1016/j.ejrs.2024.02.005
Yang Liu, Quanhua Zhao, Shuhan Jia, Yu Li
Aiming at solving the quality and efficiency problems of village extraction in large-scale remote sensing images, this paper proposes a lightweight large-scale village extraction method that integrates deep transitive transfer learning and attention mechanism. The lightweight MobileNet v2 is used as the backbone network to solve the time-consuming problem of traditional Xception backbone network. The deep and shallow features are enhanced by introducing an attention mechanism to further improve the accuracy of village extraction. The deep transitive transfer learning strategy is used to solve the problems of wrong extraction and fragmentation of extracted villages caused by insufficient sample size in large-scale extraction, and realize the effective extraction of large-scale remote sensing image villages. First, pre-train the lightweight Deeplab v3 + network with the SBD dataset to obtain the SBD pre-training weights. Then, Sentinel-2 dataset and Landsat-8 dataset were used to further train the lightweight Deeplab v3 + network successively with the SBD pre-trained weights. Then the trained proposed the lightweight Deeplab v3 + network was used to extract village from large-scale RS images. The experimental results show that the algorithm in this paper can reduce the training time. The accuracy indicators OA is 98.40 %, the Kappa reaches 0.8641, are all higher than the comparison methods. In the transferability experiment of the verification model, the OA of the proposed algorithm is above 98 %, the Kappa is above 0.83. It shows that the proposed algorithm is transferable. The proposed algorithm is applied to the Liaoning Province which village scene is complex for experiment. The result shows that it can effectively extract rural villages and has a certain generalization ability and can provide support for village monitoring in large-scale areas.
{"title":"A lightweight Large-Scale RS image village extraction method combining deep transitive transfer learning and attention mechanism","authors":"Yang Liu, Quanhua Zhao, Shuhan Jia, Yu Li","doi":"10.1016/j.ejrs.2024.02.005","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.02.005","url":null,"abstract":"<div><p>Aiming at solving the quality and efficiency problems of village extraction in large-scale remote sensing images, this paper proposes a lightweight large-scale village extraction method that integrates deep transitive transfer learning and attention mechanism. The lightweight MobileNet v2 is used as the backbone network to solve the time-consuming problem of traditional Xception backbone network. The deep and shallow features are enhanced by introducing an attention mechanism to further improve the accuracy of village extraction. The deep transitive transfer learning strategy is used to solve the problems of wrong extraction and fragmentation of extracted villages caused by insufficient sample size in large-scale extraction, and realize the effective extraction of large-scale remote sensing image villages. First, pre-train the lightweight Deeplab v3 + network with the SBD dataset to obtain the SBD pre-training weights. Then, Sentinel-2 dataset and Landsat-8 dataset were used to further train the lightweight Deeplab v3 + network successively with the SBD pre-trained weights. Then the trained proposed the lightweight Deeplab v3 + network was used to extract village from large-scale RS images. The experimental results show that the algorithm in this paper can reduce the training time. The accuracy indicators <em>OA</em> is 98.40 %, the <em>Kappa</em> reaches 0.8641, are all higher than the comparison methods. In the transferability experiment of the verification model, the <em>OA</em> of the proposed algorithm is above 98 %, the <em>Kappa</em> is above 0.83. It shows that the proposed algorithm is transferable. The proposed algorithm is applied to the Liaoning Province which village scene is complex for experiment. The result shows that it can effectively extract rural villages and has a certain generalization ability and can provide support for village monitoring in large-scale areas.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 192-203"},"PeriodicalIF":6.4,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000115/pdfft?md5=da32cd4fcfa2f88b6091e740da5729e2&pid=1-s2.0-S1110982324000115-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139975895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates (UAE). The current study aims to delineate AGR potential zone mapping using the traditional analytical hierarchy process (AHP) and a hybrid deep learning model namely, Convolutional Neural Network-Xtreme Gradient Boosting (CNN-XGB) was used for the optimal prediction-based suitability assessment. A total of nine hydrogeological factors were considered for AGR mapping. First, the influence of each parameter was determined based on expert opinion and literature reviews for the AHP approach (0.007 consistency ratio). Second, a hybrid CNN-XGB model (90.8 % accuracy) predicted the AGR and non-AGR classes as part of binary classification and generated an AGR potential zone map. Moreover, the contributing factors were analyzed deeply for the AGR site selection to understand the intercorrelation, importance, and prediction interaction. Using both approaches, a comparative assessment was conducted in the eastern, central, and western parts of Sharjah. The AGR zone based on the CNN-XGB model achieved a precision of (0.8168), recall (0.7873), and F1-score (0.8018). The critical contributing factors for AGR mapping were found to be geology (20%), geomorphology (15%), rainfall (10%), and groundwater level (10%). The AGR map is expected to help explore new sites with potentially higher favourability to retain water, deal with water scarcity, and improve water management in the UAE.
{"title":"Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones","authors":"Rami Al-Ruzouq , Abdallah Shanableh , Ratiranjan Jena , Sunanda Mukherjee , Mohamad Ali Khalil , Mohamed Barakat A. Gibril , Biswajeet Pradhan , Nezar Atalla Hammouri","doi":"10.1016/j.ejrs.2024.02.006","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.02.006","url":null,"abstract":"<div><p>The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates (UAE). The current study aims to delineate AGR potential zone mapping using the traditional analytical hierarchy process (AHP) and a hybrid deep learning model namely, Convolutional Neural Network-Xtreme Gradient Boosting (CNN-XGB) was used for the optimal prediction-based suitability assessment. A total of nine hydrogeological factors were considered for AGR mapping. First, the influence of each parameter was determined based on expert opinion and literature reviews for the AHP approach (0.007 consistency ratio). Second, a hybrid CNN-XGB model (90.8 % accuracy) predicted the AGR and non-AGR classes as part of binary classification and generated an AGR potential zone map. Moreover, the contributing factors were analyzed deeply for the AGR site selection to understand the intercorrelation, importance, and prediction interaction. Using both approaches, a comparative assessment was conducted in the eastern, central, and western parts of Sharjah. The AGR zone based on the CNN-XGB model achieved a precision of (0.8168), recall (0.7873), and F1-score (0.8018). The critical contributing factors for AGR mapping were found to be geology (20%), geomorphology (15%), rainfall (10%), and groundwater level (10%). The AGR map is expected to help explore new sites with potentially higher favourability to retain water, deal with water scarcity, and improve water management in the UAE.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 178-191"},"PeriodicalIF":6.4,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000127/pdfft?md5=70559393859eef16c23ebee13f01bfbf&pid=1-s2.0-S1110982324000127-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139942442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1016/j.ejrs.2024.02.004
Yuhao Wu , Nan Jiang , Yan Xu , Ta-Kang Yeh , Ao Guo , Tianhe Xu , Song Li , Zhaorui Gao
In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique “accumulation” period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor.
{"title":"Revealing the water vapor transport during the Henan “7.20” heavy rainstorm based on ERA5 and Real-Time GNSS","authors":"Yuhao Wu , Nan Jiang , Yan Xu , Ta-Kang Yeh , Ao Guo , Tianhe Xu , Song Li , Zhaorui Gao","doi":"10.1016/j.ejrs.2024.02.004","DOIUrl":"10.1016/j.ejrs.2024.02.004","url":null,"abstract":"<div><p>In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique “accumulation” period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 165-177"},"PeriodicalIF":6.4,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000103/pdfft?md5=0049c91b68f59488283cce188de947d5&pid=1-s2.0-S1110982324000103-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-21DOI: 10.1016/j.ejrs.2024.02.002
Hesham M. El-Asmar , Mahmoud Sh. Felfla , Sameh B. El-Kafrawy , Ahmed Gaber , Doaa M. Naguib , Mohamed Bahgat , Hoda M. El Safty , Maysa M.N. Taha
From the 6th to 7th of February 2023, a storm surge struck Ras El-Bar, Nile Delta coast and attacked the resort facilities, with a wave height and velocity in deep water of 7.2 m and 12.7 m/sec respectively. The wind speed was 12.84 m/s, blowing from the NW and the WSW quadrants. This was an unwitnessed event revealed from the study of similar time interval from 1998 to 2022. Synchronizing with this event on the 6th of February 2023, was Kahramanmaraş Turkey Earthquakes. Consequently, the shoreline receded for about −30 m and with a drop in sea-level of about −40 cm. Furthermore, considerable changes in the beach morphology from a dissipative to a cuspate-related, intermediate tidal flat transverse bar with a rip profile. These are either related to the change in the morphodynamic or sedimentary budget, and resulting due to seawater scouring of bottom sediments for more than −30 cm. Two days preceding the Earthquakes an isostatic rise in sea-level (+20 cm) at the Turkish coast compared to the Mediterranean records, which is interpreted due to regional underwater seismic activities. The drop in the sea-surface height does not happen due to seawater outflow to the Atlantic Ocean. However, the sea-level regained its normal position because of the refill occurring from the Atlantic Ocean to the Mediterranean Sea. The pumice pieces, organic peat, and starfish distributed at Ras El-Bar coast, and thrown from the Northern Mediterranean indicate that the Egyptian coast was subjected to a little tsunami with average height of 14 cm. It is minimized due to enforced wave shifting from high pressure over Egypt to the low-pressure sinks.
{"title":"A little tsunami at Ras El-Bar, Nile Delta, Egypt; consequent to the 2023 Kahramanmaraş Turkey earthquakes","authors":"Hesham M. El-Asmar , Mahmoud Sh. Felfla , Sameh B. El-Kafrawy , Ahmed Gaber , Doaa M. Naguib , Mohamed Bahgat , Hoda M. El Safty , Maysa M.N. Taha","doi":"10.1016/j.ejrs.2024.02.002","DOIUrl":"10.1016/j.ejrs.2024.02.002","url":null,"abstract":"<div><p>From the 6<sup>th</sup> to 7<sup>th</sup> of February 2023, a storm surge struck Ras El-Bar, Nile Delta coast and attacked the resort facilities, with a wave height and velocity in deep water of 7.2 m and 12.7 m/sec respectively. The wind speed was 12.84 m/s, blowing from the NW and the WSW quadrants. This was an unwitnessed event revealed from the study of similar time interval from 1998 to 2022. Synchronizing with this event on the 6<sup>th</sup> of February 2023, was Kahramanmaraş Turkey Earthquakes. Consequently, the shoreline receded for about −30 m and with a drop in sea-level of about −40 cm. Furthermore, considerable changes in the beach morphology from a dissipative to a cuspate-related, intermediate tidal flat transverse bar with a rip profile. These are either related to the change in the morphodynamic or sedimentary budget, and resulting due to seawater scouring of bottom sediments for more than −30 cm. Two days preceding the Earthquakes an isostatic rise in sea-level (+20 cm) at the Turkish coast compared to the Mediterranean records, which is interpreted due to regional underwater seismic activities. The drop in the sea-surface height does not happen due to seawater outflow to the Atlantic Ocean. However, the sea-level regained its normal position because of the refill occurring from the Atlantic Ocean to the Mediterranean Sea. The pumice pieces, organic peat, and starfish distributed at Ras El-Bar coast, and thrown from the Northern Mediterranean indicate that the Egyptian coast was subjected to a little tsunami with average height of 14 cm. It is minimized due to enforced wave shifting from high pressure over Egypt to the low-pressure sinks.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 2","pages":"Pages 147-164"},"PeriodicalIF":6.4,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000097/pdfft?md5=3f1f5bcd545635b3b9b98dc0aee5c507&pid=1-s2.0-S1110982324000097-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}