2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS最新文献_第3页

Overview of the Standards and Metrics of Ocean Surface Vector Wind by Spaceborne Microwave Remote Sensing 星载微波遥感海洋表面矢量风的标准与度量综述

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554852

Xiaolong Dong, P. Chang, A. Stoffelen, M. Portabella, Rajeev Kuma, S. Linow, Juhong Zou, Wenming Lin, Xing-ou Xu

{"title":"Overview of the Standards and Metrics of Ocean Surface Vector Wind by Spaceborne Microwave Remote Sensing","authors":"Xiaolong Dong, P. Chang, A. Stoffelen, M. Portabella, Rajeev Kuma, S. Linow, Juhong Zou, Wenming Lin, Xing-ou Xu","doi":"10.1109/IGARSS47720.2021.9554852","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554852","url":null,"abstract":"Decades of ocean surface vector wind (OSVW) data acquired from space-based radar scatterometry have been providing short and long-term researches and applications information about ocean surfaces. The main objective of the project, stands and metrics of ocean surface vector wind by space-borne microwave remote sensing, of W orking Group on Calibration and Validation of the Committee on Earth Observation Satellites (CEOS WGCV), is to develop the standard and guideline for the requirement, procedure, processing and assessment for the space borne radar scatterometer measurement calibration, wind retrieval approaches, wind data validation and assessment for OSVW, which will be used to assure the consistency of the data quality of these satellites and instruments are the prerequisite for related scientific researches and applications. This synthesizes calibration, standardized practices of retrieval approaches for ocean surface winds, development of guidelines/standards of validation of ocean surface winds, and identifying and organizing collocation related data. This presentation will provide an overview of the proj ect and the recent progresses.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"97 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":"127163848","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

Spectral and Spatial Residual Attention Network for Joint Hyperspectral and Lidar Data Classification 高光谱与激光雷达数据联合分类的光谱与空间残差关注网络

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554312

Jing Wang, Jun Zhou, Xinwen Liu, Farah Jahan

引用次数: 2

Integrating Satellite-Derived Vegetation Variables into the ISBA Model: A Sequential Data Assimilation Approach 将卫星衍生植被变量整合到ISBA模型:一种序列数据同化方法

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553560

J. Calvet, B. Bonan, Anthony Mucia, D. Shamambo, Yongjun Zheng, C. Albergel

引用次数: 0

Characterising Spectroradiometer Instrumental Spectral Performance and Its Impact on Retrieved Reflectances 表征光谱辐射计仪器光谱性能及其对反演反射率的影响

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553320

Simon A. Trim, A. Hueni, Kimberley Mason

引用次数: 0

Principal Component Analysis Based Polynomial Chaos Expansion Regression of Leaf Area Index from Polsar Imagery 基于主成分分析的Polsar影像叶面积指数多项式混沌展开回归

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554929

M. F. Celik, E. Erten

引用次数: 0

Crop Classification from Sentinel-2 Time Series with Temporal Convolutional Neural Networks 基于时间卷积神经网络的Sentinel-2时间序列作物分类

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554358

Sara Pérez-Carabaza, V. Syrris, P. Kempeneers, P. Soille

引用次数: 1

Agglutination of Sub-Basins Using Shreve Order 利用Shreve序的子盆地凝集

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554333

S. Rosim, M. D. Martino

引用次数: 0

Urban Flood Detection Using Sentinel1-A Images 基于Sentinel1-A图像的城市洪水检测

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554283

Shadi Sadat Baghermanesh, S. Jabari, H. McGrath

{"title":"Urban Flood Detection Using Sentinel1-A Images","authors":"Shadi Sadat Baghermanesh, S. Jabari, H. McGrath","doi":"10.1109/IGARSS47720.2021.9554283","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554283","url":null,"abstract":"Synthetic Aperture Radar (SAR) imagery plays a vital role in flood mapping due to the day/night, almost all-weather, and cloud penetration capabilities. Although SAR backscatter intensity can accurately identify flooded areas on bare soil, it is still challenging to classify flooded urban areas due to the complexity of urban structures. Polarimetric SAR (PolSAR) and Interferometric SAR (InSAR) can provide us with a robust identification of backscatter patterns in urban areas, including single-bounce and double-bounce backscatters. In this study, we explore the potential of PolSAR and InSAR in urban flood mapping using a Random Forest model. The study area is located in Fredericton, New Brunswick, along the Saint John River, which has a long history of flooding. We examined various combinations of PolSAR and InSAR features, derived from Sentinel-1A images, along with four other features that are well-known to contribute to flooding, to select the best features for the model. The results showed that employing Polarimetric and Interferometric SAR (PolInSAR) features together with land-use/land-cover, altitude, slope, and aspect layers, reached an 88.6% flood classification accuracy in urban areas.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"29 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":"126026175","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

SAR Remote Sensing of Marine Surface Films 海洋表面膜SAR遥感

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553968

M. Gade

引用次数: 0

Deep Learning Application for Fracture Segmentation Over Outcrop Images from UAV-Based Digital Photogrammetry 基于无人机的数字摄影测量露头图像裂缝分割的深度学习应用

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553232

Ademir Marques, Graciela Racolte, E. Souza, Hiduino Venâncio Domingos, Rafael Kenji Horota, J. G. Motta, D. Zanotta, C. Cazarin, L. Gonzaga, M. Veronez

{"title":"Deep Learning Application for Fracture Segmentation Over Outcrop Images from UAV-Based Digital Photogrammetry","authors":"Ademir Marques, Graciela Racolte, E. Souza, Hiduino Venâncio Domingos, Rafael Kenji Horota, J. G. Motta, D. Zanotta, C. Cazarin, L. Gonzaga, M. Veronez","doi":"10.1109/IGARSS47720.2021.9553232","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553232","url":null,"abstract":"Fractures affect the intrinsic properties of permeability and porosity of reservoir geobodies, making its network characterization an important task for fluid flow modeling. Direct acquisition of data on reservoirs is labor-intensive and generally produces sparse information. Thus, the study of analogue outcrops with similar characteristics is often carried out by using unmanned aerial vehicle image acquisition and digital photogrammetry. However, the accurate automatic recognition of the fractures network over the outcrop images remains a challenge. Image segmentation methods based on convolution neural networks (CNNs) were successfully applied in medicine, biology, and other areas, however, not yet in geological fracture detection. This work proposes the validation of two popular CNNs - Segnet and Unet - for pixel-to-pixel segmentation targeting fracture detection. Initial results showed acceptable scores of the metrics mean intersection over union (mIoU) and dice intersection (F1) in both CNNs.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"11 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":"126083174","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