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2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS最新文献

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Scanning GNSS-R Beams from Cubesats Using Sequentially Rotated Deployable Dipoles 利用顺序旋转可展开偶极子扫描来自立方体卫星的GNSS-R波束
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554649
Sokolow Valentin, Farzad Jabbarigargari, P. Fisette, C. Craeye
{"title":"Scanning GNSS-R Beams from Cubesats Using Sequentially Rotated Deployable Dipoles","authors":"Sokolow Valentin, Farzad Jabbarigargari, P. Fisette, C. Craeye","doi":"10.1109/IGARSS47720.2021.9554649","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554649","url":null,"abstract":"GNSS-R reflectometry uses direct and reflected signals from Global Positioning satellites to probe the Earth's surface. This paper focuses on constellations of tiny satellites creating single scanned beams. Special attention is paid to the capability to deploy easily a limited number of antennas. Here, circular polarization and limited scanning are made possible through the deployment of four linearly polarized antennas. Sidelobes are controlled using parasitic elements, and the favorable effect of the cube on back-radiation is demonstrated.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116665642","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}
引用次数: 0
A Contrario Oil Tank Detection with Patch Match Completion 一种具有补丁匹配完成功能的反向油箱检测方法
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553048
A. Tadros, S. Drouyer, R. G. V. Gioi
{"title":"A Contrario Oil Tank Detection with Patch Match Completion","authors":"A. Tadros, S. Drouyer, R. G. V. Gioi","doi":"10.1109/IGARSS47720.2021.9553048","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553048","url":null,"abstract":"The energy sector is a key industry in the global economy and monitoring oil storage provides valuable insights into the economic state of a country. Our aim is to detect oil tank farms as accurately as possible using Sentinel-2 images. An a contrario clustering method is used to group by density the result of a circle detection step. Then, a patch-match procedure is used to complete the tank detection. Although most existing methods are designed to work on high-resolution images, the proposed method is designed for low-resolution images; we also propose an adaptation to high-resolution images.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116801853","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}
引用次数: 1
Remote Estimation of Sulfur Content in Fuel from SO2 and CO2 Quantification of Ship Exhaust Plumes 从船舶烟气SO2和CO2的定量分析中遥测燃料中硫含量
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554102
J. Gagnon, M. Larivière-Bastien, Jacob Thibodeau, S. Boubanga-Tombet
{"title":"Remote Estimation of Sulfur Content in Fuel from SO2 and CO2 Quantification of Ship Exhaust Plumes","authors":"J. Gagnon, M. Larivière-Bastien, Jacob Thibodeau, S. Boubanga-Tombet","doi":"10.1109/IGARSS47720.2021.9554102","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554102","url":null,"abstract":"Sulfur oxide (SOx) from seagoing ships contribute to local air pollution in cities and coastal areas around the world. Sulfur dioxide (SO2) emissions in particular, are a precursor to acid rain and atmospheric particulates leading to ocean acidification which can contribute to negative human health outcomes2. The International Convention for the Prevention of Pollution from Ships (MARPOL) defines limits on the sulfur content in ship fuel oils, since the sulfur is ultimately released into the atmosphere through the ship's exhaust system as sulfur dioxide (SO2). This application note describes the use of remote hyperspectral imaging data collected using the Telops Hyper-Cam along with signal processing techniques to provide rapid and accurate estimation of sulfur content in fuel oils. Comparison between the retrieved sulfur content in the fuel of several ships with data from bunker delivery notes provided by the ship's owner and in situ measurements performed by Transport Canada are presented.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116903453","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}
引用次数: 0
Adversarial Fine-Grained Adaptation Network for Cross-Scene Classification 面向跨场景分类的对抗细粒度自适应网络
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554195
Sihan Zhu, Fulin Luo, Bo Du, Liangpei Zhang
{"title":"Adversarial Fine-Grained Adaptation Network for Cross-Scene Classification","authors":"Sihan Zhu, Fulin Luo, Bo Du, Liangpei Zhang","doi":"10.1109/IGARSS47720.2021.9554195","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554195","url":null,"abstract":"Domain adaptation is widely used in the field of remote sensing, which can transfer the existing knowledge to new tasks and promote performance. When applied in the field of scene classification, it can be called cross-scene classification. Previous cross-scene classification methods mainly consider the coarse-grained alignment in the global aspect, which may ignore the structures behind the data and lose the local information with respect to specific categories. To implement fine-grained alignment, we present an adversarial fine-grained adaptation network (AFGAN) which simultaneously captures the complex structures behind the data distributions to improve the discriminability and reduce the local discrepancy of different domains to align the relevant category distributions. Experimental results based on three existing scene classification datasets demonstrate the effectiveness of AFGAN.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116915825","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}
引用次数: 5
Hyperspectral Image Classification Based on Spectral Graph and Bidirectional LSTM Network 基于谱图和双向LSTM网络的高光谱图像分类
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553035
Xu Tang, Qionglin Zhou, Fanbo Meng, Xiao Han, Dalei Li, Xiangrong Zhang, L. Jiao
{"title":"Hyperspectral Image Classification Based on Spectral Graph and Bidirectional LSTM Network","authors":"Xu Tang, Qionglin Zhou, Fanbo Meng, Xiao Han, Dalei Li, Xiangrong Zhang, L. Jiao","doi":"10.1109/IGARSS47720.2021.9553035","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553035","url":null,"abstract":"Convolutional neural networks (CNNs) have achieved cracking performance in the hyperspectral image (HSI) classification task. Nevertheless, most of them cannot meet what we expect when the numbers of labeled samples are small. Also, due to the rectangular convolution kernels, the long-range context information within HSIs is cannot fully be explored. To solve these problems, we propose a semi-supervised method based on the graph convolutional network (GCN) and bidirectional Long Short-Term Memory (Bi-LSTM). First, HSIs are over segmented into various superpixels and GCN is employed for mining the advanced spectral features. Second, we input the obtained spectral features to the Bi-LSTM model for exploring global spatial features. Due to the diverse receptive fields, the short-and long-range spatial relations can be discovered simultaneously. Finally, we map the features from region-level to pixel-level for classifying HSIs. The positive experimental results counted on two HSIs demonstrate that our method is superior to some popular methods.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117018200","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}
引用次数: 1
Experimental Comparison of Registration Methods for Multisensor Sar-Optical Data 多传感器sar光学数据配准方法的实验比较
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553640
B. Pinel-Puysségur, Luca Maggiolo, M. Roux, Nicolas Gasnier, David Solarna, G. Moser, S. Serpico, F. Tupin
{"title":"Experimental Comparison of Registration Methods for Multisensor Sar-Optical Data","authors":"B. Pinel-Puysségur, Luca Maggiolo, M. Roux, Nicolas Gasnier, David Solarna, G. Moser, S. Serpico, F. Tupin","doi":"10.1109/IGARSS47720.2021.9553640","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553640","url":null,"abstract":"Synthetic aperture radar (SAR) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117172528","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}
引用次数: 3
Solar-Induced Chlorophyll Fluorescence is Very Sensitive to Drought 太阳诱导的叶绿素荧光对干旱非常敏感
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553404
Ruonan Qiu, Ge Han, Xin Ma, Wei Gong
{"title":"Solar-Induced Chlorophyll Fluorescence is Very Sensitive to Drought","authors":"Ruonan Qiu, Ge Han, Xin Ma, Wei Gong","doi":"10.1109/IGARSS47720.2021.9553404","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553404","url":null,"abstract":"Continued drought can lead to vegetation mortality and reduced carbon sink capacity of terrestrial ecosystems. However, the complexity of the causes and processes of drought has led to a limited understanding of how vegetation performances under drought. Here we used solar-induced chlorophyll fluorescence (SIF) and enhanced vegetation index (EVI) data to explore the impact of U.S Midwest drought on vegetation in 2012. At the whole study area and flux tower scale, SIF is more sensitive to the decrease in precipitation than EVI. SIF also can more accurately monitor the growth of vegetation under drought. SIF is an effective index for monitoring environmental stress on vegetation.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117346432","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}
引用次数: 0
Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation 使绿色交通成为现实:一种基于分类的数据分析方法来确定适合电动汽车充电点安装的特性
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553748
J. Flynn, E. Brealy, C. Giannetti
{"title":"Making Green Transport a Reality: A Classification Based Data Analysis Method to Identify Properties Suitable for Electric Vehicle Charging Point Installation","authors":"J. Flynn, E. Brealy, C. Giannetti","doi":"10.1109/IGARSS47720.2021.9553748","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553748","url":null,"abstract":"With Electric Vehicles (EVs) emerging as the dominant mode of green transportation in the UK, it is critical that local authorities and urban planners can accurately map the existing EV infrastructures in place. In this paper, we demonstrate a novel data processing pipeline to analyse remotely sensed image data to highlight areas of a city most suitable for EV infrastructure. By applying deep transfer learning to multiple datasets, we are able to identify individual addresses suitable for the installation of home EV charging points. Using this same methodology, we also highlight areas where community charging points would be most effectively installed. We improve on previous methods by integrating topographical data, Census data, and remotely sensed image data to achieve a fully automated system capable of large-scale surveying of external building characteristics.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"49 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120882941","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}
引用次数: 2
Segmentation of Sentinel-1 SAR Images Over the Ocean, Preliminary Methods and Assessments Sentinel-1海洋SAR图像分割的初步方法与评价
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553429
A. Colin, C. Peureux, R. Husson, N. Longépé, Régis Rauzy, Ronan Fablet, P. Tandeo, Samir Saoudi, A. Mouche, G. Dibarboure
{"title":"Segmentation of Sentinel-1 SAR Images Over the Ocean, Preliminary Methods and Assessments","authors":"A. Colin, C. Peureux, R. Husson, N. Longépé, Régis Rauzy, Ronan Fablet, P. Tandeo, Samir Saoudi, A. Mouche, G. Dibarboure","doi":"10.1109/IGARSS47720.2021.9553429","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553429","url":null,"abstract":"Segmentations of ocean SAR images (Sentinel-1 A and B) into 10 classes of metoceanic phenomena are for the first time presented, with a 400 m resolution. Ocean SAR images segmentation differs from classic deep learning problems with a high variety of shapes and a particular importance of high-frequency patterns. To this end, an assessment of deep learning frameworks is performed, with a focus on the comparison between weakly supervised and supervised methods. Metrics based on the Wassertein distance indicate best performances by the supervised segmentation (U-Net) given operational constraints, thus highlighting the significance of properly annotated data sets. While available training data sets are made of small $20 times 20 text{km}$ imagettes, the extension of the inference from imagettes to wide swath images, with a wider variety of incidence angles, presents promising results and opens the way to more extensive oceanographic applications in SAR imagery.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120947137","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}
引用次数: 5
A Novel Data Augmentation Method for SAR Image Target Detection and Recognition 一种新的SAR图像目标检测与识别数据增强方法
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553275
Xiaolong Zhang, Xinghua Chai, Yanqiao Chen, Zichen Yang, Guangyuan Liu, Aiyuan He, Yangyang Li
{"title":"A Novel Data Augmentation Method for SAR Image Target Detection and Recognition","authors":"Xiaolong Zhang, Xinghua Chai, Yanqiao Chen, Zichen Yang, Guangyuan Liu, Aiyuan He, Yangyang Li","doi":"10.1109/IGARSS47720.2021.9553275","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553275","url":null,"abstract":"With the development of remote sensing satellite technology, the resolution of remote sensing images is constantly improved, but there are difficulties in obtaining labeled SAR image datasets for target detection and recognition. To address the problem that only limited SAR image target detection and recognition data are available, a novel data augmentation method based on convolutional neural network is proposed. Firstly, the Synthetic Aperture Radar (SAR) image target detection and recognition dataset SAR_OD was produced based on the synthesis of military targets and background images in MSTAR dataset. But considering the fact that the number of targets in each image in SAR_OD is still not enough for training a target detection model with good performance, we augmented SAR_OD and then we obtained SAR_OD+ dataset. It is proved that the model trained on SAR_OD+ dataset is significantly improved in the evaluation index by the data augmentation method proposed in this paper, especially in the experiments using only 50% of the training data. Therefore, the proposed data augmentation method can be used to improve the performance of SAR image target detection and recognition model in the case of limited labeled data.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"53 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121010575","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}
引用次数: 1
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