发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Ammonia Nitrogen Monitoring of Urban Rivers with UAV-Borne Hyperspectral Remote Sensing Imagery 基于无人机高光谱遥感影像的城市河流氨氮监测
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554632
Zhou Wang, Lifei Wei, Chujun He, Qikai Lu
{"title":"Ammonia Nitrogen Monitoring of Urban Rivers with UAV-Borne Hyperspectral Remote Sensing Imagery","authors":"Zhou Wang, Lifei Wei, Chujun He, Qikai Lu","doi":"10.1109/IGARSS47720.2021.9554632","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554632","url":null,"abstract":"Ammonia nitrogen (NH4-N) can cause water eutrophication and is the main oxygen-consuming pollutant in water bodies. Remote sensing methods are more macroscopic than traditional measurement methods. However, due to the weak optical characteristics of NH4-N, traditional remote sensing data cannot meet the needs of NH4-N monitoring. In response to this problem, this paper attempts to use unmanned aerial vehicles (UAV) hyperspectral imagery combined with extreme gradient boosting (XGBoost)regression algorithm to quantitatively retrieve NH4-N in urban rivers. The results show that compared with the traditional empirical semi-empirical model, the accuracy of using the XGBoost algorithm to estimate the NH4-N in the water body is significantly improved, and is consistent with the field measurement.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"112 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":"115006385","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: 4
Generating Winter Wheat Global Crop Calendars in the Framework of Worldcereal 在世界谷物框架下生成冬小麦全球作物日历
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553083
Juanma Cintas Rodríguez, B. Franch, I. Becker-Reshef, S. Skakun, J. Sobrino, K. V. Tricht, J. Degerickx, S. Gilliams
{"title":"Generating Winter Wheat Global Crop Calendars in the Framework of Worldcereal","authors":"Juanma Cintas Rodríguez, B. Franch, I. Becker-Reshef, S. Skakun, J. Sobrino, K. V. Tricht, J. Degerickx, S. Gilliams","doi":"10.1109/IGARSS47720.2021.9553083","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553083","url":null,"abstract":"In this study we present a methodology to develop a global winter wheat crop calendar based on the existing crop calendar products from FAO and GEOGLAM Crop Monitor in the framework of the WorldCereal project. It is based on integrating both datasets by building on the accuracy from Crop Monitor and the spatial resolution from the Food and Agriculture Organization of the United Nations (FAO). Additionally, given the global extent of WorldCereal and the gaps that both products present at global scale, we simulated the crop calendars in those areas not covered by any of the products. To do so, we integrated a Regression-Kriging model considering as training data the calendars derived from both products and based on the latitude, height and distance to the coast (DTC).","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"5 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":"115137589","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
Wave Scattering from a Modulated Rough Surface 调制粗糙表面的波散射
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554990
Ying Yang, Kun Shan Chen
{"title":"Wave Scattering from a Modulated Rough Surface","authors":"Ying Yang, Kun Shan Chen","doi":"10.1109/IGARSS47720.2021.9554990","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554990","url":null,"abstract":"We illustrate the wave scattering from a multiscale rough surface modeled by a modulated correlation function. The modulation ratio, defined as the ratio of baseband correlation length and the modulated length, determines the degree of multiscale roughness. The dependence of bistatic scattering on the modulation ratio are investigated. Numerical results show that without considering the multiscale roughness, the scattering coefficients are overestimated at a small incident angle region but underestimated at a large scattering region. Radar wave scattering from multiscale rough surface is highly frequency selective. As an application example, we compare the model predictions with two independent sets of measurement data. The results demonstrate that the model predictions with modulation effects are in good agreement with the measurement data.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"118 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":"115596774","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 Machine Learning Methodology for Next Day Wildfire Prediction 次日野火预测的机器学习方法
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554301
Stella Girtsou, Alexis Apostolakis, G. Giannopoulos, C. Kontoes
{"title":"A Machine Learning Methodology for Next Day Wildfire Prediction","authors":"Stella Girtsou, Alexis Apostolakis, G. Giannopoulos, C. Kontoes","doi":"10.1109/IGARSS47720.2021.9554301","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554301","url":null,"abstract":"In this paper, we handle the problem of next day wildfire prediction via the use of machine learning. In contrast to most works in the relevant literature, we set the problem to its realistic basis, with respect to its large scale, the extreme imbalance in the data distribution, the required high spatial granularity of the predictions and the consideration of the strong spatial correlations inherent in the data. We implement a machine learning workflow that exploits Tree Ensemble and Neural Network algorithms, upon which an extensive hyperparameter search procedure is performed, via cross-validation, in order to select a set of effective models that are expected to generalize well on new data. Our experiments on the whole Greek territory demonstrate the effectiveness of the proposed methodology, rendering it directly applicable to real-world scenarios. Finally, several insights towards further improving the effectiveness of current models are discussed.","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":"115621052","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
Multi-Category SAR Images Generation Based on Improved Generative Adversarial Network 基于改进生成对抗网络的多类SAR图像生成
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554120
Shaoyan Du, Jun Hong, Yu Wang, Kaichu Xing, Tian Qiu
{"title":"Multi-Category SAR Images Generation Based on Improved Generative Adversarial Network","authors":"Shaoyan Du, Jun Hong, Yu Wang, Kaichu Xing, Tian Qiu","doi":"10.1109/IGARSS47720.2021.9554120","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554120","url":null,"abstract":"The generative adversarial network (GAN) provides a different way for SAR data augmentation. The traditional GAN model is mainly based on the Jensen-Shannon (JS) divergence or Wasserstein distance. The former faces mode collapse, while the latter is not suitable for multi-category image generation. In this paper, an improved model based on WGAN-GP is proposed. An encoder is used to learn the features of real samples as the input of the generator to control training to a certain extent and make the generated image quality better. In addition, a pre-trained classifier is introduced as the constraint of the generator to ensure the generated images have the correct category information. MSTAR dataset is used to verify the generation capability of the proposed model. The results show that the proposed model has the stable generation capability to provide high-quality SAR images as a supplementary training dataset, which could assist in achieving good classification accuracy.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"197 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":"115710849","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
Comparison of Optical and SAR Data for Deforestation Mapping in the Amazon Rainforest with Fully Convolutional Networks 基于全卷积网络的亚马逊雨林毁林制图光学与SAR数据比较
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554970
M. O. Adarme, R. Feitosa, J. Bermudez, P. Happ, C. Almeida
{"title":"Comparison of Optical and SAR Data for Deforestation Mapping in the Amazon Rainforest with Fully Convolutional Networks","authors":"M. O. Adarme, R. Feitosa, J. Bermudez, P. Happ, C. Almeida","doi":"10.1109/IGARSS47720.2021.9554970","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554970","url":null,"abstract":"Early detection of deforestation processes is vital to maintain and regulate tropical rainforests, such as in the Amazon region. Most of them rely on optical imagery. Approaches based on Synthetic Aperture Radar (SAR) data are comparatively unexplored, in particular for deforestation detection in tropical rainforests. This work addresses this gap and evaluates Fully Convolutional Networks based on the U-Net, Res-Unet and Siamese Network, for deforestation detection using images from three different sensors, Landsat-8, Sentinel-2, and Sentinel-1. Experiments conducted on a dataset of the Amazon rainforest indicated that Fully Convolutional Networks working on Sentinel-1 data can achieve sufficient accuracy for detecting deforestation in tropical rainforests when clouds prevent the use of optical data11The source code is available in https://github.zcom/MabelOrtega/Comparison-of-Optical-and-SAR-data-for-deforestation-mapping-in-the-Amazon-Forest-with-FCN.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"79 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":"123046919","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: 4
SAR Image Change Detection Method Based on Neural-CRF Structure 基于神经- crf结构的SAR图像变化检测方法
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553563
Jianlong Zhang, Mengying Cui, Bin Wang
{"title":"SAR Image Change Detection Method Based on Neural-CRF Structure","authors":"Jianlong Zhang, Mengying Cui, Bin Wang","doi":"10.1109/IGARSS47720.2021.9553563","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553563","url":null,"abstract":"There are two problems in SAR image change detection when using difference images (DIs), i.e., 1) the subtraction operation results in serious loss of semantic information in DIs; and 2) the boundary of DI is uncertain. We propose a change detection method based on Neural-CRF structure. Firstly, Transformer-UNet (TR-UNet) is designed to provide the unary potential for CRF. The TR-Attention module improves the semantic expression ability of UNet by introducing the multi-head attention mechanism of TR. Secondly, a cascade CRF as Recurrent Neural Network, named as C-CRF-RNN, is proposed to update the unary potential and pairwise potential simultaneously. This improves the ability of CRF-RNN to refine pixel-level label prediction. Experiments show that the proposed method consistently outperforms the state-of-the-art methods on two benchmarks including berne data and ottawa data.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"108 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":"114651461","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
Sharpening the 20 M Bands of SENTINEL-2 Image Using an Unsupervised Convolutional Neural Network 基于无监督卷积神经网络的SENTINEL-2图像20m波段锐化
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9555082
H. Nguyen, M. Ulfarsson, J. R. Sveinsson
{"title":"Sharpening the 20 M Bands of SENTINEL-2 Image Using an Unsupervised Convolutional Neural Network","authors":"H. Nguyen, M. Ulfarsson, J. R. Sveinsson","doi":"10.1109/IGARSS47720.2021.9555082","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9555082","url":null,"abstract":"This paper proposes a novel method for sharpening the 20 m bands of the multispectral images acquired by the Sentinel-2 (S2) constellation. We formulate the S2 sharpening as an inverse problem and solve it using an unsupervised convolutional neural network (CNN), called S2UCNN. The proposed method extends the deep image prior provided by a CNN structure with S2 domain knowledge. We incorporate a modulation transfer function-based degradation model as a network layer. We add the 10 m bands to both the network input and output to take advantage of the multitask learning. Experimental results with a real S2 dataset show that the proposed method outperforms the competitive methods on reduced-resolution data and gives very high quality sharpened image on full-resolution data.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"103 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":"115742229","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
Global Long-Term Brightness Temperature Record from L-Band SMOS and Smap Observations 来自l波段SMOS和Smap观测的全球长期亮温记录
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554579
Xiaojun Li, J. Wigneron, F. Frappart, L. Fan, G. Lannoy, A. Konings, Xiangzhuo Liu, Mengjia Wang, R. Fernandez-Moran, A. Al-Yaari, Honagliang Ma, Zanping Xing, C. Moisy
{"title":"Global Long-Term Brightness Temperature Record from L-Band SMOS and Smap Observations","authors":"Xiaojun Li, J. Wigneron, F. Frappart, L. Fan, G. Lannoy, A. Konings, Xiangzhuo Liu, Mengjia Wang, R. Fernandez-Moran, A. Al-Yaari, Honagliang Ma, Zanping Xing, C. Moisy","doi":"10.1109/IGARSS47720.2021.9554579","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554579","url":null,"abstract":"Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear fusion method. We found that SMOS and SMAP TB are strongly correlated (R > 0.90 over most of the globe) but present a small bias at both the horizontal and vertical polarizations. The preliminary evaluation results show that this bias can be adjusted using a linear fit, but further evaluation procedures are still needed. In the near future, we will develop a long-term time series of SM and L-VOD products based on this merged SMOS-SMAP TB record.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"41 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":"124277671","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
Annual Crop Classification Experiments in Portugal Using Sentinel-2 基于Sentinel-2的葡萄牙年度作物分类试验
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9555009
P. Benevides, H. Costa, Francisco D. Moreira, Daniel Moraes, M. Caetano
{"title":"Annual Crop Classification Experiments in Portugal Using Sentinel-2","authors":"P. Benevides, H. Costa, Francisco D. Moreira, Daniel Moraes, M. Caetano","doi":"10.1109/IGARSS47720.2021.9555009","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9555009","url":null,"abstract":"This paper presents an experimental crop classification of the 10 most abundant annual crop types in Portugal, using a study area located in Alentejo region. This region has great diversity of land uses as well as multiple crop types. Sentinel-2 2018 intra-annual time-series imagery is considered in the experiment. The Portuguese Land Parcel Identification System (LPIS) is used to extract automatic training samples. LPIS information is automatically processed with the help of auxiliary datasets to filter out crop areas more likely to have been mislabeled. Classification is obtained using random forest. Validation is performed using an independent dataset also based on LPIS. A global accuracy of 76% is obtained. The novelty of the methodology here presented shows that LPIS can be used together with auxiliary data for crop type mapping, helping to characterize the agriculture land diversity in Portugal.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"137 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":"125236225","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
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信