Pub Date : 2022-01-01DOI: 10.23977/geors.2022.050104
Danling Chen
: Spatial data is the core and operation object of geographic information system (GIS). The quality of spatial data determines the application of GIS and the effectiveness of decision-making to a great extent. This article introduces two important types of spatial data, vector data and raster data. Then, this paper discusses the uncertainty and sources of errors in spatial data, and discusses the methods of checking and preventing uncertainty and errors from the aspects and processes of digitization, so as to ensure the quality of spatial data. Finally, this paper explores cutting-edge approaches to improving spatial data quality, including the Area preserving method for improved categorical raster resampling, and using hierarchical grid index to detect and correct errors in vector elevation data. By studying effective data quality control methods, the quality of spatial data in GIS can be guaranteed, and the basic guarantee for the wide application and development of geographic information science can be provided.
{"title":"Reviewing Methods for Controlling Spatial Data Quality from Multiple Perspectives","authors":"Danling Chen","doi":"10.23977/geors.2022.050104","DOIUrl":"https://doi.org/10.23977/geors.2022.050104","url":null,"abstract":": Spatial data is the core and operation object of geographic information system (GIS). The quality of spatial data determines the application of GIS and the effectiveness of decision-making to a great extent. This article introduces two important types of spatial data, vector data and raster data. Then, this paper discusses the uncertainty and sources of errors in spatial data, and discusses the methods of checking and preventing uncertainty and errors from the aspects and processes of digitization, so as to ensure the quality of spatial data. Finally, this paper explores cutting-edge approaches to improving spatial data quality, including the Area preserving method for improved categorical raster resampling, and using hierarchical grid index to detect and correct errors in vector elevation data. By studying effective data quality control methods, the quality of spatial data in GIS can be guaranteed, and the basic guarantee for the wide application and development of geographic information science can be provided.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"7 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86937193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3012523
Xiaoqian Mou, Xiaolong Chen, J. Guan, Yunlong Dong, Ningbo Liu
The problem of strong sea clutter, e.g., sea spikes, may bring in low signal-to-clutter ratio (SCR) and cause great interference to radar marine target detection. However, the sea clutter suppression ability of current algorithms is limited with poor generalization under complex marine environment. In this letter, a novel sea clutter suppression generative adversarial network (SCS-GAN) is designed and employed for marine radar plan-position indicator (PPI) images detection. The SCS-GAN is based on residual networks and attention module, which includes residual attention generator (RAG) and sea clutter discriminator (SCD). In order to expand the data sets and improve generalization ability, clutter-free data set A, simulated sea clutter data set B (containing five types of sea clutter distributions), and actual sea clutter data set C are constructed by means of simulation and acquisition of real radar returns. At last, the parameter, i.e., clutter suppression ratio (CSR) is designed for evaluating the sea clutter suppression performances of the proposed method and other denoising and clutter suppression methods including CBM3D, denoising convolutional neural network (DnCNN), FFDNet, and Pix2pix. After testing with actual data, it is proved that the SCS-GAN has faster clutter removal speed, stronger generalization ability, and at the same time marine targets in images are remained completely.
{"title":"Sea Clutter Suppression for Radar PPI Images Based on SCS-GAN","authors":"Xiaoqian Mou, Xiaolong Chen, J. Guan, Yunlong Dong, Ningbo Liu","doi":"10.1109/lgrs.2020.3012523","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3012523","url":null,"abstract":"The problem of strong sea clutter, e.g., sea spikes, may bring in low signal-to-clutter ratio (SCR) and cause great interference to radar marine target detection. However, the sea clutter suppression ability of current algorithms is limited with poor generalization under complex marine environment. In this letter, a novel sea clutter suppression generative adversarial network (SCS-GAN) is designed and employed for marine radar plan-position indicator (PPI) images detection. The SCS-GAN is based on residual networks and attention module, which includes residual attention generator (RAG) and sea clutter discriminator (SCD). In order to expand the data sets and improve generalization ability, clutter-free data set A, simulated sea clutter data set B (containing five types of sea clutter distributions), and actual sea clutter data set C are constructed by means of simulation and acquisition of real radar returns. At last, the parameter, i.e., clutter suppression ratio (CSR) is designed for evaluating the sea clutter suppression performances of the proposed method and other denoising and clutter suppression methods including CBM3D, denoising convolutional neural network (DnCNN), FFDNet, and Pix2pix. After testing with actual data, it is proved that the SCS-GAN has faster clutter removal speed, stronger generalization ability, and at the same time marine targets in images are remained completely.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1886-1890"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3012523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46788870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3011074
Xiran Zhou, Jiawei Chen, Todd E. Rakstad, M. Ploughe, P. Tang
Water quality, which is a key concern associated with large-scale canal operation and management, is vulnerable to the influences from short-term weather variations and artificial activities. Chlorophyll is one of the key indicators to measure the water quality and usability for drinking and irrigation in the canal system. However, previous research designed the state-of-the-art algorithms regarding water chlorophyll estimation using medium-resolution remote sensing data (e.g., Landsat), which has insufficient resolution to capture canals that are usually narrower than one pixel in such data. High-resolution imageries covering the whole canal network might include only either visible wavebands (i.e., red, green, blue bands) or cost thousands of dollars for an effective investigation on real-time water chlorophyll monitoring. Thus, the strategy designed for water chlorophyll analysis in a canal should consider an appropriate tradeoff among spatial resolution, the spectrum helpful for chlorophyll detection, and the financial burden. This letter presents our efforts on identifying and assessing the extent of the Planet data for measuring chlorophyll degree of canal waters. The experiments show that although Planet can represent the relative variation in water chlorophyll concentration, new algorithms are still necessary for accurate results regarding water chlorophyll variations in a canal system.
{"title":"Water Chlorophyll Estimation in an Urban Canal System With High-Resolution Remote Sensing Data","authors":"Xiran Zhou, Jiawei Chen, Todd E. Rakstad, M. Ploughe, P. Tang","doi":"10.1109/lgrs.2020.3011074","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3011074","url":null,"abstract":"Water quality, which is a key concern associated with large-scale canal operation and management, is vulnerable to the influences from short-term weather variations and artificial activities. Chlorophyll is one of the key indicators to measure the water quality and usability for drinking and irrigation in the canal system. However, previous research designed the state-of-the-art algorithms regarding water chlorophyll estimation using medium-resolution remote sensing data (e.g., Landsat), which has insufficient resolution to capture canals that are usually narrower than one pixel in such data. High-resolution imageries covering the whole canal network might include only either visible wavebands (i.e., red, green, blue bands) or cost thousands of dollars for an effective investigation on real-time water chlorophyll monitoring. Thus, the strategy designed for water chlorophyll analysis in a canal should consider an appropriate tradeoff among spatial resolution, the spectrum helpful for chlorophyll detection, and the financial burden. This letter presents our efforts on identifying and assessing the extent of the Planet data for measuring chlorophyll degree of canal waters. The experiments show that although Planet can represent the relative variation in water chlorophyll concentration, new algorithms are still necessary for accurate results regarding water chlorophyll variations in a canal system.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1876-1880"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43807209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3009823
Li Dalin, W. Haijiao, Yang Zhen, Guan Yanfeng, Shen Shi
The provision of real-time information services is one of the crucial functions of satellites. In comparison with the centralized scheduling, the distributed scheduling can provide better robustness and extendibility. However, the existing distributed satellite scheduling algorithms require a large amount of communication between satellites to coordinate tasks, which makes it difficult to support scheduling in real-time. This letter proposes a multiagent deep reinforcement learning (MADRL)-based method to solve the problem of scheduling real-time multisatellite cooperative observation. The method enables satellites to share their decision policy, but it is not necessary to share data on the decisions they make or data on their current internal state. The satellites can use the decision policy to infer the decisions of other satellites to decide whether to accept a task when they receive a new request for observations. In this way, our method can significantly reduce the communication overhead and improve the response time. The pillar of the architecture is a multiagent deep deterministic policy gradient network. Our simulation results show that the proposed method is stable and effective. In comparison with the Contract Net Protocol method, our algorithm can reduce the communication overhead and achieve better use of satellite resources.
{"title":"An Online Distributed Satellite Cooperative Observation Scheduling Algorithm Based on Multiagent Deep Reinforcement Learning","authors":"Li Dalin, W. Haijiao, Yang Zhen, Guan Yanfeng, Shen Shi","doi":"10.1109/lgrs.2020.3009823","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3009823","url":null,"abstract":"The provision of real-time information services is one of the crucial functions of satellites. In comparison with the centralized scheduling, the distributed scheduling can provide better robustness and extendibility. However, the existing distributed satellite scheduling algorithms require a large amount of communication between satellites to coordinate tasks, which makes it difficult to support scheduling in real-time. This letter proposes a multiagent deep reinforcement learning (MADRL)-based method to solve the problem of scheduling real-time multisatellite cooperative observation. The method enables satellites to share their decision policy, but it is not necessary to share data on the decisions they make or data on their current internal state. The satellites can use the decision policy to infer the decisions of other satellites to decide whether to accept a task when they receive a new request for observations. In this way, our method can significantly reduce the communication overhead and improve the response time. The pillar of the architecture is a multiagent deep deterministic policy gradient network. Our simulation results show that the proposed method is stable and effective. In comparison with the Contract Net Protocol method, our algorithm can reduce the communication overhead and achieve better use of satellite resources.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1901-1905"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3009823","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45056927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3011743
Jiankai Li, Yuguo Li, Y. Liu, K. Spitzer, B. Han
To investigate the effect of azimuthal anisotropy on frequency-domain marine controlled-source electromagnetic (CSEM) responses, an adaptive edge-based finite-element (FE) modeling algorithm is presented in this letter. The 3-D algorithm is capable of dealing with generally anisotropic conductive media. It is implemented on unstructured tetrahedral grids, which allow for complex model geometries. The accuracy of the FE solution is controlled through adaptive mesh refinement, which is performed iteratively until the solution converges to the desired accuracy tolerance. The algorithm is validated against the quasi-analytic solutions for a 1-D layered model with anisotropy. We then simulate the marine CSEM responses over a set of 3-D anisotropic models and illustrate that the azimuthal anisotropy has a considerable influence on both the inline and broadside marine CSEM responses but to different extents.
{"title":"3-D Marine CSEM Forward Modeling With General Anisotropy Using an Adaptive Finite-Element Method","authors":"Jiankai Li, Yuguo Li, Y. Liu, K. Spitzer, B. Han","doi":"10.1109/lgrs.2020.3011743","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3011743","url":null,"abstract":"To investigate the effect of azimuthal anisotropy on frequency-domain marine controlled-source electromagnetic (CSEM) responses, an adaptive edge-based finite-element (FE) modeling algorithm is presented in this letter. The 3-D algorithm is capable of dealing with generally anisotropic conductive media. It is implemented on unstructured tetrahedral grids, which allow for complex model geometries. The accuracy of the FE solution is controlled through adaptive mesh refinement, which is performed iteratively until the solution converges to the desired accuracy tolerance. The algorithm is validated against the quasi-analytic solutions for a 1-D layered model with anisotropy. We then simulate the marine CSEM responses over a set of 3-D anisotropic models and illustrate that the azimuthal anisotropy has a considerable influence on both the inline and broadside marine CSEM responses but to different extents.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1936-1940"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011743","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45683302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3011151
Jie Chen, Jingru Zhu, Geng Sun, Jianhui Li, M. Deng
Semantic segmentation of high-resolution remote sensing imagery (HRSI) is a major task in remote sensing analysis. Although deep convolutional neural network (DCNN)-based semantic segmentation models have powerful capacity in pixel-wise classification, they still face challenge in obtaining intersemantic continuity and extraboundary accuracy because of the geo-object’s characteristic feature of diverse scales and various distributions in HRSI. Inspired by the transfer learning, in this study, we propose an efficient semantic segmentation framework named SMAF-Net, which shares multiscale adversarial features into a U-shaped semantic segmentation model. Specifically, it uses multiscale adversarial feature representation obtained from a well-trained generative adversarial network to grasp the pixel correlation and further improve the boundary accuracy of multiscale geo-objects. Comparison experiments on the Potsdam and Vaihingen data sets demonstrate that the proposed framework can achieve considerable improvement in the semantic segmentation of HRSI.
{"title":"SMAF-Net: Sharing Multiscale Adversarial Feature for High-Resolution Remote Sensing Imagery Semantic Segmentation","authors":"Jie Chen, Jingru Zhu, Geng Sun, Jianhui Li, M. Deng","doi":"10.1109/lgrs.2020.3011151","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3011151","url":null,"abstract":"Semantic segmentation of high-resolution remote sensing imagery (HRSI) is a major task in remote sensing analysis. Although deep convolutional neural network (DCNN)-based semantic segmentation models have powerful capacity in pixel-wise classification, they still face challenge in obtaining intersemantic continuity and extraboundary accuracy because of the geo-object’s characteristic feature of diverse scales and various distributions in HRSI. Inspired by the transfer learning, in this study, we propose an efficient semantic segmentation framework named SMAF-Net, which shares multiscale adversarial features into a U-shaped semantic segmentation model. Specifically, it uses multiscale adversarial feature representation obtained from a well-trained generative adversarial network to grasp the pixel correlation and further improve the boundary accuracy of multiscale geo-objects. Comparison experiments on the Potsdam and Vaihingen data sets demonstrate that the proposed framework can achieve considerable improvement in the semantic segmentation of HRSI.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1921-1925"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3011151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42034558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this “nonstill image” problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged $R^{2}$ is higher than 0.9 and the error is less than $5 ~mu text {g/m}^{3}$ .
控制区域臭氧污染需要连续的空间知识。从地面站点进行的测量有助于实现这一目标，但由于站点建立、操作和维护的巨大费用，其数量有限。遥感似乎是一个很有前途的数据来源，但其应用受到恶劣天气条件的挑战。重庆是中国西部人口稠密的工业城市，常年阴云密布，臭氧污染严重，但相关研究相对不足。另一种选择是通过模型估算臭氧。在重庆，由于地形非常复杂，性能良好的模型出现了退化。模拟的每小时臭氧与地面测量值不一致。因此，提出了一种优化方法来改进模型对这些区域的估计。这种方法通过使用ResNet(残差网)整合地面信息(例如，测量的臭氧和气象)。ResNet克服了经典神经网络中臭名昭著的梯度消失问题，极大地提高了学习复杂系统的能力。臭氧的分布就像一幅每秒钟都在变化的灰色图像，这不是ResNet通常学到的情况。提出了一种类似彩色图像的数据结构来解决这种“非静止图像”问题;根据泰勒展开，多项式可以描述一个复杂的系统，并且误差是可以接受的。为了方便在业务操作中的使用，此方法被设计为健壮、廉价且易于使用。详细讨论了控制选址方案。在交叉验证中，该方法表现良好，平均$R^{2}$大于0.9，误差小于$5 ~mu text {g/m}^{3}$。
{"title":"An Optimization Approach for Hourly Ozone Simulation: A Case Study in Chongqing, China","authors":"Songyan Zhu, Qiaolin Zeng, Hao Zhu, Jian Xu, Jianbin Gu, Yongqian Wang, Liangfu Chen","doi":"10.1109/lgrs.2020.3010416","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3010416","url":null,"abstract":"Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this “nonstill image” problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged $R^{2}$ is higher than 0.9 and the error is less than $5 ~mu text {g/m}^{3}$ .","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1871-1875"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3010416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43797425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3012164
Hai-Li Zhang, Xingyue Guo, Y. Sha, Xiao-Yang He, M. Xia
In this letter, an appropriate approach is proposed for modeling the electromagnetic (EM) scattering from composite rough surfaces made up of wake due to a submerged body and wind-driven sea waves. The computational fluid dynamics (CFD) method is used to extract the air–seawater surface wake generated by an underwater moving body at different speeds and depths. Then, the wake is superimposed on the randomly rough wind-driven sea surfaces that obey the Pierson–Moskowitz power spectrum. The small slope approximation (SSA) method is adopted to calculate the EM scattering by the composite surfaces. The simulation results are obtained and justified.
{"title":"Modeling of EM Scattering by Composite Surfaces Made of Wake Due to a Submerged Body and Wind-Driven Sea Waves","authors":"Hai-Li Zhang, Xingyue Guo, Y. Sha, Xiao-Yang He, M. Xia","doi":"10.1109/lgrs.2020.3012164","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3012164","url":null,"abstract":"In this letter, an appropriate approach is proposed for modeling the electromagnetic (EM) scattering from composite rough surfaces made up of wake due to a submerged body and wind-driven sea waves. The computational fluid dynamics (CFD) method is used to extract the air–seawater surface wake generated by an underwater moving body at different speeds and depths. Then, the wake is superimposed on the randomly rough wind-driven sea surfaces that obey the Pierson–Moskowitz power spectrum. The small slope approximation (SSA) method is adopted to calculate the EM scattering by the composite surfaces. The simulation results are obtained and justified.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1881-1885"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3012164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48674268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3010591
Lukui Shi, Ziyuan Wang, Bin Pan, Zhenwei Shi
It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.
{"title":"An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation","authors":"Lukui Shi, Ziyuan Wang, Bin Pan, Zhenwei Shi","doi":"10.1109/lgrs.2020.3010591","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3010591","url":null,"abstract":"It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1896-1900"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3010591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43030005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/lgrs.2020.3010827
Yun Shi, Peiliang Xu
Measurements in remote sensing geodesy have been well known to be of speckle noise nature. Although a number of despeckling algorithms have been proposed mainly based on the local weighted statistics in the engineering literature, there are relatively few studies on the statistical adjustment methods for processing the measurements contaminated with the speckle or multiplicative errors. We develop the least squares (LS)-based adjustment methods for the remote sensing measurements with multiplicative errors and trends, evaluate the accuracy of the parameter estimates, and derive the corresponding formulas to estimate the variance of the unit weight. Simulation examples are used to illustrate the developed theory and methods.
{"title":"Adjustment of Measurements With Multiplicative Random Errors and Trends","authors":"Yun Shi, Peiliang Xu","doi":"10.1109/lgrs.2020.3010827","DOIUrl":"https://doi.org/10.1109/lgrs.2020.3010827","url":null,"abstract":"Measurements in remote sensing geodesy have been well known to be of speckle noise nature. Although a number of despeckling algorithms have been proposed mainly based on the local weighted statistics in the engineering literature, there are relatively few studies on the statistical adjustment methods for processing the measurements contaminated with the speckle or multiplicative errors. We develop the least squares (LS)-based adjustment methods for the remote sensing measurements with multiplicative errors and trends, evaluate the accuracy of the parameter estimates, and derive the corresponding formulas to estimate the variance of the unit weight. Simulation examples are used to illustrate the developed theory and methods.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"18 1","pages":"1916-1920"},"PeriodicalIF":4.8,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/lgrs.2020.3010827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42320227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：