Systematic assessment of ballast fouling and mechanized cleaning efficiency through ground penetrating radar (GPR) is vital to ensure track stability and safe train transportation. Nevertheless, conventional methods of ballast fouling inspection and evaluation impede construction progress and escalate the cost of maintenance. This paper proposes a novel method using random irregular polygons and collision detection algorithms to model the ballast layer and simulated using the finite-difference time-domain (FDTD) algorithm. Hilbert transform energy, S-transform, and energy integration curve are employed to identify ballast fouling and cleaning efficiency. The highly fouled ballast exhibits concentrated Hilbert transform energy, increased energy attenuation rate in S-transform with depth in the 1.0-3.0 GHz, along with a stronger energy integration curve. Clean or post-cleaning ballast shows opposite results. Experiments on a passenger trunk line in southern China validated the method’s accuracy after mechanized ballast cleaning. This approach guides GPR-based detection and supports railway maintenance. Future studies will consider heterogeneous properties and the three-dimensional structure of the ballast layer.
{"title":"Identification of Ballast Fouling Status and Mechanized Cleaning Efficiency Using FDTD Method","authors":"Bo Li, Zhan Peng, Shi-Hua Wang, Linyan Guo","doi":"10.3390/rs15133437","DOIUrl":"https://doi.org/10.3390/rs15133437","url":null,"abstract":"Systematic assessment of ballast fouling and mechanized cleaning efficiency through ground penetrating radar (GPR) is vital to ensure track stability and safe train transportation. Nevertheless, conventional methods of ballast fouling inspection and evaluation impede construction progress and escalate the cost of maintenance. This paper proposes a novel method using random irregular polygons and collision detection algorithms to model the ballast layer and simulated using the finite-difference time-domain (FDTD) algorithm. Hilbert transform energy, S-transform, and energy integration curve are employed to identify ballast fouling and cleaning efficiency. The highly fouled ballast exhibits concentrated Hilbert transform energy, increased energy attenuation rate in S-transform with depth in the 1.0-3.0 GHz, along with a stronger energy integration curve. Clean or post-cleaning ballast shows opposite results. Experiments on a passenger trunk line in southern China validated the method’s accuracy after mechanized ballast cleaning. This approach guides GPR-based detection and supports railway maintenance. Future studies will consider heterogeneous properties and the three-dimensional structure of the ballast layer.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83922267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modeling the quantitative relationship between target components and measured spectral information is an essential part of laser-induced breakdown spectroscopy (LIBS) analysis. However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.
{"title":"When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks","authors":"Yan Yu, Meibao Yao","doi":"10.3390/rs15133422","DOIUrl":"https://doi.org/10.3390/rs15133422","url":null,"abstract":"Modeling the quantitative relationship between target components and measured spectral information is an essential part of laser-induced breakdown spectroscopy (LIBS) analysis. However, many traditional multivariate analysis algorithms must reduce the spectral dimension or extract the characteristic spectral lines in advance, which may result in information loss and reduced accuracy. Indeed, improving the precision and interpretability of LIBS quantitative analysis is a critical challenge in Mars exploration. To solve this problem, this paper proposes an end-to-end lightweight quantitative modeling framework based on ensemble convolutional neural networks (ECNNs). This method eliminates the need for dimensionality reduction of the raw spectrum along with other pre-processing operations. We used the ChemCam calibration dataset as an example to verify the effectiveness of the proposed approach. Compared with partial least squares regression (a linear method) and extreme learning machine (a nonlinear method), our proposed method resulted in a lower root-mean-square error for major element prediction (54% and 73% lower, respectively) and was more stable. We also delved into the internal learning mechanism of the deep CNN model to understand how it hierarchically extracts spectral information features. The experimental results demonstrate that the easy-to-use ECNN-based regression model achieves excellent prediction performance while maintaining interpretability.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91392714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luying Wang, Siyuan Wang, Xiaofei Liang, Xuebing Jiang, Jiping Wang, Chuang Li, Shihui Chang, Y. You, Kai Su
Identifying and protecting key sites of ecological assets and improving spatial connectivity and accessibility are important measures taken to protect ecological diversity. This study takes Guangxi as the research area. Based on the gross ecosystem product (GEP), the ecological source is identified, and the initial ecological network (EN) is constructed by identifying the ecological corridor with the minimum cumulative resistance model. The internal defects of the initial ecological network are extracted using the circuit theory, the priority areas for restoration and protection with clear spatial positions are determined according to the complex network analysis, and the network’s performance before and after optimization is comprehensively evaluated. The results show that 456 initial ecological sources and 1219 ecological corridors have been identified, forming the initial ecological network of Guangxi. Based on the circuit theory, 168 ecological barriers, 83 ecological pinch points, and 71 ecological stepping stones were extracted for network optimization. After optimizing the ecological network, there are 778 ecological sources with a total area of 73,950.56 km2 and 2078 ecological corridors with a total length of 23,922.07 km. The GEP of the optimized structure is 13.33% higher than that of the non-optimized structure. The priority areas for protection are distributed in a large area, and the attached GEP reaches USD 118 billion, accounting for 72% of the total GEP attached to the optimized ecological source area. The priority areas for restoration are scattered in small patches, with a GEP of USD 19.27 billion. The robustness and connectivity of the optimized ecological network have been improved obviously. This study attempts to identify key sites of ecological assets and the priority regions for restoration and conservation using genuine geographical location and reference materials for regional ecological network optimization and implementation.
{"title":"How to Optimize High-Value GEP Areas to Identify Key Areas for Protection and Restoration: The Integration of Ecology and Complex Networks","authors":"Luying Wang, Siyuan Wang, Xiaofei Liang, Xuebing Jiang, Jiping Wang, Chuang Li, Shihui Chang, Y. You, Kai Su","doi":"10.3390/rs15133420","DOIUrl":"https://doi.org/10.3390/rs15133420","url":null,"abstract":"Identifying and protecting key sites of ecological assets and improving spatial connectivity and accessibility are important measures taken to protect ecological diversity. This study takes Guangxi as the research area. Based on the gross ecosystem product (GEP), the ecological source is identified, and the initial ecological network (EN) is constructed by identifying the ecological corridor with the minimum cumulative resistance model. The internal defects of the initial ecological network are extracted using the circuit theory, the priority areas for restoration and protection with clear spatial positions are determined according to the complex network analysis, and the network’s performance before and after optimization is comprehensively evaluated. The results show that 456 initial ecological sources and 1219 ecological corridors have been identified, forming the initial ecological network of Guangxi. Based on the circuit theory, 168 ecological barriers, 83 ecological pinch points, and 71 ecological stepping stones were extracted for network optimization. After optimizing the ecological network, there are 778 ecological sources with a total area of 73,950.56 km2 and 2078 ecological corridors with a total length of 23,922.07 km. The GEP of the optimized structure is 13.33% higher than that of the non-optimized structure. The priority areas for protection are distributed in a large area, and the attached GEP reaches USD 118 billion, accounting for 72% of the total GEP attached to the optimized ecological source area. The priority areas for restoration are scattered in small patches, with a GEP of USD 19.27 billion. The robustness and connectivity of the optimized ecological network have been improved obviously. This study attempts to identify key sites of ecological assets and the priority regions for restoration and conservation using genuine geographical location and reference materials for regional ecological network optimization and implementation.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85860888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weicheng Song, Aiqing Feng, Guojie Wang, Qixia Zhang, Wen Dai, Xikun Wei, Yifan Hu, S. Amankwah, Feihong Zhou, Yi Liu
Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.
准确评估作物分布范围和绘制不同作物类型的地图对于监测和管理现代农业至关重要。中、高空间分辨率遥感(RS)对地观测和深度学习(DL)是作物制图最主要、最有效的工具之一。在这项研究中，我们使用来自Google Earth Engine (GEE)的高分辨率Sentinel-2图像对中国安徽省蚌埠市的水稻和冬小麦进行了绘制。我们在改进的DeepLabv3+架构、Segformer和随机森林(RF)中比较了不同流行的深度学习骨干网络与传统机器学习(ML)方法的性能，包括HRNet、MobileNet、Xception和Swin Transformer。结果表明，基于Transformer架构编码器和轻量级多层感知器(MLP)解码器组合的Segformer总体精度(OA)值为91.06%，平均F1 Score (mF1)值为89.26%，平均Intersection over Union (mIoU)值为80.70%。Segformer通过结合多个评估指标的结果优于其他深度学习方法。除Swin Transformer在OA中略低于RF外，所有DL方法在主要映射对象的精度上均显著优于RF方法，mIoU提高约13.5~26%。利用Segformer预测的水稻和冬小麦图像具有成图精度高、田边清晰、细节特征鲜明、误分类率低等特点。因此，深度学习是一种基于遥感影像快速准确定位水稻和冬小麦的有效选择。
{"title":"Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks","authors":"Weicheng Song, Aiqing Feng, Guojie Wang, Qixia Zhang, Wen Dai, Xikun Wei, Yifan Hu, S. Amankwah, Feihong Zhou, Yi Liu","doi":"10.3390/rs15133417","DOIUrl":"https://doi.org/10.3390/rs15133417","url":null,"abstract":"Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90384491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Hashim, Babangida Baiya, M. Mahmud, D. Sani, M. M. Chindo, M. Tan, A. B. Pour
Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6.
土地利用-土地覆盖(LULC)的变化通过影响产水量(WY)以及降雨和蒸散发(ET)的变化来影响一个地区的水分平衡。遥感卫星图像提供了一个全面的LULC时空分布，可以分析大范围内WY的变化。因此，本研究通过特别比较基于卫星和现场衍生的WY，并描述WY变化与流域内LULC变化幅度的关系，绘制和分析了2000年至2015年间柔佛河流域(JRB) LULC和WY的连续变化。水分平衡方程通过降水减去ET的平衡来确定水分平衡。降水和ET信息分别来源于热带降雨测量任务(TRMM)和中分辨率成像光谱仪(MODIS)卫星数据。LULC地图提取自Landsat- enhanced Thematic Mapper Plus (ETM+)和Landsat Operational Land Imager (OLI)。结果表明，基于卫星的导出量与现场测量值之间具有良好的一致性，降水和蒸散发的平均偏差分别为±20.04 mm和±43 mm。2000 - 2015年LULC变化表明，除油棕外的农业用地增加11.07%，森林减少32.15%，油棕增加11.88%，城市用地增加9.82%，导致WY增加15.76%。这一发现可以作为基于卫星的WY和LULC变化的关键举措，以实现联合国可持续发展目标(UNSDG) 6的具体目标6.1和6.2。
{"title":"Analysis of Water Yield Changes in the Johor River Basin, Peninsular Malaysia Using Remote Sensing Satellite Imagery","authors":"M. Hashim, Babangida Baiya, M. Mahmud, D. Sani, M. M. Chindo, M. Tan, A. B. Pour","doi":"10.3390/rs15133432","DOIUrl":"https://doi.org/10.3390/rs15133432","url":null,"abstract":"Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90731917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nan Jiang, H. Du, Shaodi Ge, Jiahua Zhu, Dong Feng, Jian Wang, Xiaotao Huang
Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.
{"title":"High-Resolution Azimuth Missing Data SAR Imaging Based on Sparse Representation Autofocusing","authors":"Nan Jiang, H. Du, Shaodi Ge, Jiahua Zhu, Dong Feng, Jian Wang, Xiaotao Huang","doi":"10.3390/rs15133425","DOIUrl":"https://doi.org/10.3390/rs15133425","url":null,"abstract":"Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76629409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the applications of lidars for remote sensing of the environment have been expanding and deepening. Among them, continuous-wave (CW) range-resolved (RR) S-lidars (S comes from Scheimpflug) have proven to be a new and promising class of non-contact and non-perturbing laser sensors. They use low-power CW diode lasers, an unconventional depth-of-field extension technique and the latest advances in nanophotonic technologies to realize compact and cost-effective remote sensors. The purpose of this paper is to propose a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path. To set the desired far and near borders of operating range by adjusting the optical transceiver, it was shown how to properly select the lens plane and image plane tilt angles, as well as the focal length, the lidar base, etc. For a generalized analysis of characteristic relations between S-lidar parameters, we introduced several dimensionless factors and criteria applicable to different range scales, including an S-lidar-specific magnification factor, angular function, dynamic range, “one and a half” condition, range-domain quality factor, etc. It made possible to show how to reasonably select named and dependent non-energetic parameters, adapting them to specific applications. Finally, we turned to the synthesis task by demonstrating ways to achieve a compromise between a wide dynamic range and high range resolution requirements. The results of the conducted analysis and synthesis allow increasing the validity of design solutions for further promotion of S-lidars for environmental remote sensing and their better adaptation to a broad spectrum of specific applications and range scales.
{"title":"Designing CW Range-Resolved Environmental S-Lidars for Various Range Scales: From a Tabletop Test Bench to a 10 km Path","authors":"R. Agishev, Zhenzhu Wang, Dong Liu","doi":"10.3390/rs15133426","DOIUrl":"https://doi.org/10.3390/rs15133426","url":null,"abstract":"In recent years, the applications of lidars for remote sensing of the environment have been expanding and deepening. Among them, continuous-wave (CW) range-resolved (RR) S-lidars (S comes from Scheimpflug) have proven to be a new and promising class of non-contact and non-perturbing laser sensors. They use low-power CW diode lasers, an unconventional depth-of-field extension technique and the latest advances in nanophotonic technologies to realize compact and cost-effective remote sensors. The purpose of this paper is to propose a generalized methodology to justify the selection of a set of non-energetic S-lidar parameters for a wide range of applications and distance scales, from a bench-top test bed to a 10-km path. To set the desired far and near borders of operating range by adjusting the optical transceiver, it was shown how to properly select the lens plane and image plane tilt angles, as well as the focal length, the lidar base, etc. For a generalized analysis of characteristic relations between S-lidar parameters, we introduced several dimensionless factors and criteria applicable to different range scales, including an S-lidar-specific magnification factor, angular function, dynamic range, “one and a half” condition, range-domain quality factor, etc. It made possible to show how to reasonably select named and dependent non-energetic parameters, adapting them to specific applications. Finally, we turned to the synthesis task by demonstrating ways to achieve a compromise between a wide dynamic range and high range resolution requirements. The results of the conducted analysis and synthesis allow increasing the validity of design solutions for further promotion of S-lidars for environmental remote sensing and their better adaptation to a broad spectrum of specific applications and range scales.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76610788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.
{"title":"Research on High-Resolution Reconstruction of Marine Environmental Parameters Using Deep Learning Model","authors":"Yaning Hu, Liwen Ma, Yushi Zhang, Zhe Wu, Jiaji Wu, Jinpeng Zhang, Xiaoxiao Zhang","doi":"10.3390/rs15133419","DOIUrl":"https://doi.org/10.3390/rs15133419","url":null,"abstract":"The analysis of marine environmental parameters plays a significant role in various aspects, including sea surface target detection, the monitoring of the marine ecological environment, marine meteorology and disaster forecasting, and the monitoring of internal waves in the ocean. In particular, for sea surface target detection, the accurate and high-resolution input of marine environmental parameters is crucial for multi-scale sea surface modeling and the prediction of sea clutter characteristics. In this paper, based on the low-resolution wind speed, significant wave height, and wave period data provided by ECMWF for the surrounding seas of China (specified latitude and longitude range), a deep learning model based on a residual structure is proposed. By introducing an attention module, the model effectively addresses the poor modeling performance of traditional methods like nearest neighbor interpolation and linear interpolation at the edge positions in the image. Experimental results demonstrate that with the proposed approach, when the spatial resolution of wind speed increases from 0.5° to 0.25°, the results achieve a mean square error (MSE) of 0.713, a peak signal-to-noise ratio (PSNR) of 49.598, and a structural similarity index measure (SSIM) of 0.981. When the spatial resolution of the significant wave height increases from 1° to 0.5°, the results achieve a MSE of 1.319, a PSNR of 46.928, and an SSIM of 0.957. When the spatial resolution of the wave period increases from 1° to 0.5°, the results achieve a MSE of 2.299, a PSNR of 44.515, and an SSIM of 0.940. The proposed method can generate high-resolution marine environmental parameter data for the surrounding seas of China at any given moment, providing data support for subsequent sea surface modeling and for the prediction of sea clutter characteristics.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72672981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ground-penetrating radar (GPR) has been extensively utilized in deep-space exploration. However, GPR modeling commonly employs simplified antenna models and carrier-free impulse signals, resulting in reduced accuracy and interpretability. In this paper, we addressed these limitations by combining a tilted monopole antenna and linear frequency modulation continuous wave (LFMCW) to simulate real conditions. Additionally, a radiation-pattern-compensation back-propagation (RPC-BP) algorithm was developed to improve the illumination of the right-inclined structure. We first introduced the LFMCW used by the Mars Rover Penetrating Radar (RoPeR) onboard the Zhurong rover, where frequencies range from 15 to 95 MHz. Although the LFMCW signal improves radiation efficiency, it increases data processing complexity. Then, the radiation patterns and response of the tilted monopole antenna were analyzed, where the radiated signal amplitude varies with frequency. Finally, a series of numerical and laboratory experiments were conducted to interpret the real RoPeR data. The results indicate that hyperbolic echoes tilt in the opposite direction of the survey direction. This study demonstrates that forward modeling considering real transmit signals and complex antenna models can improve modeling accuracy and prevent misleading interpretations on deep-space exploration missions. Moreover, the migration process can improve imaging quality by considering radiation pattern compensation.
{"title":"Mars Rover Penetrating Radar Modeling and Interpretation Considering Linear Frequency Modulation Source and Tilted Antenna","authors":"Shichao Zhong, Yibo Wang, Yikang Zheng, Ling Chen","doi":"10.3390/rs15133423","DOIUrl":"https://doi.org/10.3390/rs15133423","url":null,"abstract":"Ground-penetrating radar (GPR) has been extensively utilized in deep-space exploration. However, GPR modeling commonly employs simplified antenna models and carrier-free impulse signals, resulting in reduced accuracy and interpretability. In this paper, we addressed these limitations by combining a tilted monopole antenna and linear frequency modulation continuous wave (LFMCW) to simulate real conditions. Additionally, a radiation-pattern-compensation back-propagation (RPC-BP) algorithm was developed to improve the illumination of the right-inclined structure. We first introduced the LFMCW used by the Mars Rover Penetrating Radar (RoPeR) onboard the Zhurong rover, where frequencies range from 15 to 95 MHz. Although the LFMCW signal improves radiation efficiency, it increases data processing complexity. Then, the radiation patterns and response of the tilted monopole antenna were analyzed, where the radiated signal amplitude varies with frequency. Finally, a series of numerical and laboratory experiments were conducted to interpret the real RoPeR data. The results indicate that hyperbolic echoes tilt in the opposite direction of the survey direction. This study demonstrates that forward modeling considering real transmit signals and complex antenna models can improve modeling accuracy and prevent misleading interpretations on deep-space exploration missions. Moreover, the migration process can improve imaging quality by considering radiation pattern compensation.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87925207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wei Yan, Y. Wang, Xiaofei Ma, Minghua Liu, Junhui Yan, Yaogeng Tan, Sutao Liu
As a significant component of the cryosphere, snow cover plays a crucial role in modulating atmospheric circulation and regional hydrological equilibrium. Therefore, studying the dynamics of snow cover and its response to climate change is of great significance for regional water resource management and disaster prevention. In this study, reanalysis climate datasets and a new MODIS snow cover extent product over China were used to analyze the characteristics of climate change and spatiotemporal variations in snow cover in the Keriya River Basin (KRB). Furthermore, the effects of climate factors on snow cover and their coupling effects on runoff were quantitatively evaluated by adopting partial least squares regression (PLSR) method and structural equation modeling (SEM), respectively. Our findings demonstrated the following: (1) Air temperature and precipitation of KRB showed a significant increase at rates of 0.24 °C/decade and 14.21 mm/decade, respectively, while the wind speed did not change significantly. (2) The snow cover frequency (SCF) in the KRB presented the distribution characteristics of “low in the north and high in the south”. The intra-annual variation of snow cover percentage (SCP) of KRB displayed a single peak (in winter), double peaks (in spring and autumn), and stability (SCP > 75%), whose boundary elevations were 4000 m and 6000 m, respectively. The annual, summer, and winter SCP in the KRB declined, while the spring and autumn SCP experienced a trend showing an insignificant increase during the hydrological years of 2001–2020. Additionally, both the annual and seasonal SCF (except autumn) will be further increased in more than 50% of the KRB, according to estimates. (3) Annual and winter SCF were controlled by precipitation, of which the former showed a mainly negative response, while the latter showed a mainly positive response, accounting for 43.1% and 76.16% of the KRB, respectively. Air temperature controlled SCF changes in 45% of regions in spring, summer, and autumn, mainly showing negative effects. Wind speed contributed to SCF changes in the range of 11.23% to 26.54% across annual and seasonal scales. (4) Climate factors and snow cover mainly affect annual runoff through direct influences, and the total effect was as follows: precipitation (0.609) > air temperature (−0.122) > SCP (0.09).
{"title":"Snow Cover and Climate Change and Their Coupling Effects on Runoff in the Keriya River Basin during 2001-2020","authors":"Wei Yan, Y. Wang, Xiaofei Ma, Minghua Liu, Junhui Yan, Yaogeng Tan, Sutao Liu","doi":"10.3390/rs15133435","DOIUrl":"https://doi.org/10.3390/rs15133435","url":null,"abstract":"As a significant component of the cryosphere, snow cover plays a crucial role in modulating atmospheric circulation and regional hydrological equilibrium. Therefore, studying the dynamics of snow cover and its response to climate change is of great significance for regional water resource management and disaster prevention. In this study, reanalysis climate datasets and a new MODIS snow cover extent product over China were used to analyze the characteristics of climate change and spatiotemporal variations in snow cover in the Keriya River Basin (KRB). Furthermore, the effects of climate factors on snow cover and their coupling effects on runoff were quantitatively evaluated by adopting partial least squares regression (PLSR) method and structural equation modeling (SEM), respectively. Our findings demonstrated the following: (1) Air temperature and precipitation of KRB showed a significant increase at rates of 0.24 °C/decade and 14.21 mm/decade, respectively, while the wind speed did not change significantly. (2) The snow cover frequency (SCF) in the KRB presented the distribution characteristics of “low in the north and high in the south”. The intra-annual variation of snow cover percentage (SCP) of KRB displayed a single peak (in winter), double peaks (in spring and autumn), and stability (SCP > 75%), whose boundary elevations were 4000 m and 6000 m, respectively. The annual, summer, and winter SCP in the KRB declined, while the spring and autumn SCP experienced a trend showing an insignificant increase during the hydrological years of 2001–2020. Additionally, both the annual and seasonal SCF (except autumn) will be further increased in more than 50% of the KRB, according to estimates. (3) Annual and winter SCF were controlled by precipitation, of which the former showed a mainly negative response, while the latter showed a mainly positive response, accounting for 43.1% and 76.16% of the KRB, respectively. Air temperature controlled SCF changes in 45% of regions in spring, summer, and autumn, mainly showing negative effects. Wind speed contributed to SCF changes in the range of 11.23% to 26.54% across annual and seasonal scales. (4) Climate factors and snow cover mainly affect annual runoff through direct influences, and the total effect was as follows: precipitation (0.609) > air temperature (−0.122) > SCP (0.09).","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80918271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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