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IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium最新文献

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Melting Glacier: A 37-Year (1984–2020) High-Resolution Glacier-Cover Record of MT. Kilimanjaro 冰川融化:乞力马扎罗山37年(1984-2020)高分辨率冰川覆盖记录
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883229
Shuai Yuan, Juepeng Zheng, Lixian Zhang, Runmin Dong, Yile Xing, Yuhan She, H. Fu, Ray C. C. Cheung
{"title":"Melting Glacier: A 37-Year (1984–2020) High-Resolution Glacier-Cover Record of MT. Kilimanjaro","authors":"Shuai Yuan, Juepeng Zheng, Lixian Zhang, Runmin Dong, Yile Xing, Yuhan She, H. Fu, Ray C. C. Cheung","doi":"10.1109/IGARSS46834.2022.9883229","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883229","url":null,"abstract":"Commonly recognized as an important symbol of the tropics and global warming, the glacier loss on Mt. Kilimanjaro has received worldwide attention for decades. In this paper, we propose a high-resolution glacier-cover (GC) record of Mt. Kilimanjaro over the period from 1984 to 2020, using a novel deep learning-based semantic segmentation method and Google Earth images, as well as digital elevation model (DEM) and ERA5-Land (ERA5) for snowline and temperature variations analysis. Our method achieves an accuracy of 94.37%, which proves the model's capability to record the GC areas precisely. The results show that (1) the GC area dramatically decreases from 19.2 km2 to 3.6 km2 during 37 years, which decreases about 4% and 2% per year from 1984 to 2000 and from 2000 to 2020 respectively, (2) the snowline altitude rises from $4,651 m$ to $5,088 m$ by about $437 m$, and (3) the average $5,000 m$ air temperature on Mt. Kilimanjaro increases from −2.1 °C to −1.1 °C by about 1 °C. This study indicates that there will be no GC within a few decades if the current loss continues.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116286888","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 Grand Collaboration in Support of Digital Twin Studies: Geoscience, Sensing, and Healthcloud 支持数字孪生研究的大合作:地球科学、传感和健康云
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884575
Chun-Yen Chang, W. Sullivan
{"title":"A Grand Collaboration in Support of Digital Twin Studies: Geoscience, Sensing, and Healthcloud","authors":"Chun-Yen Chang, W. Sullivan","doi":"10.1109/IGARSS46834.2022.9884575","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884575","url":null,"abstract":"In recent years, there has been a burst of discovery regarding the health benefits people gain from having contact with green landscapes [1]. These discoveries describe the salutary benefits of green urban and rural landscapes [2], small scale [3] and regional scale green landscapes [4]. Investigators have explored the impacts of varying levels of vegetation in landscapes on health outcomes such as mood [5], exercise [6], neural activities [7], COVID-19 [8], capacity to pay attention [9], [10], safety [11], and general health [12].","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121495058","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
Nonconvex-NLTV Regularization-Based SAR Image Feature Enhancement with Water Body Information Extraction Using QILU-1 SAR Data 基于非凸nltv正则化SAR图像特征增强和水体信息提取的QILU-1 SAR数据
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883891
Zhongqiu Xu, Mingzhi Wang, Bingchen Zhang, Yirong Wu, Suihua Liu, Ou Ruan
{"title":"Nonconvex-NLTV Regularization-Based SAR Image Feature Enhancement with Water Body Information Extraction Using QILU-1 SAR Data","authors":"Zhongqiu Xu, Mingzhi Wang, Bingchen Zhang, Yirong Wu, Suihua Liu, Ou Ruan","doi":"10.1109/IGARSS46834.2022.9883891","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883891","url":null,"abstract":"Synthetic aperture radar (SAR) images have been widely used in water body information extraction. However, SAR images suffer from speckles and the additive noise, which affect the performance of automatic information extraction. Thus, we propose the nonconvex-nonlocal total variation (NLTV) regularization to suppress speckles and the additive noise, and improve the performance of water body information extraction using the enhanced images. Experiments using Qilu-1 (QL-1) SAR data verify the effectiveness of the method.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121540861","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
Analysis of Polar Firn Density and Grain Size Models using Available Data 利用现有数据分析极地雪密度和粒度模型
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883418
Dua Kaurejo, M. Aksoy, R. Kar
{"title":"Analysis of Polar Firn Density and Grain Size Models using Available Data","authors":"Dua Kaurejo, M. Aksoy, R. Kar","doi":"10.1109/IGARSS46834.2022.9883418","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883418","url":null,"abstract":"This paper provides a brief analysis of existing depthdependent models of the physical properties of the polar firn using available data for subsurface density and grain radius. Results show that across inland Antarctica, firn density increases exponentially with depth. The density has gaussian fluctuations with negative damping. Grain radius increases linearly with depth for up to a few hundred meters. A standardized dataset for in-situ measurements of density and grain radius is formed and input parameters for their depthdependent models are summarized in this paper.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121544737","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
Improving Freeze/Thaw Onsets Retrieval by Combining SMAP and AMSR2 Based on Xgboost: a Case Study in Alaska 基于Xgboost的SMAP和AMSR2联合改进冻融初起反演:以阿拉斯加为例
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884884
Wen Zhong, Q. Yuan, Tingting Liu, Linwei Yue
{"title":"Improving Freeze/Thaw Onsets Retrieval by Combining SMAP and AMSR2 Based on Xgboost: a Case Study in Alaska","authors":"Wen Zhong, Q. Yuan, Tingting Liu, Linwei Yue","doi":"10.1109/IGARSS46834.2022.9884884","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884884","url":null,"abstract":"Passive microwave remote sensing can effectively capture the near-surface soil freeze/thaw onsets. Accurately understanding the transition of permafrost freeze/thaw state is helpful for us to respond to climate change in time. In order to improve the retrieval accuracy of freeze/thaw onsets, we propose an XGBoost modeling method that combines SMAP and AMSR2 for freeze/thaw onsets detection. We conducted experiments using data covering Alaska from 2015 to 2020 to demonstrate the effectiveness of our method. The proposed model was applied to the whole study area to obtain the spatial and temporal distribution of freezing periods. During the study period, the shortening of the freezing period has been most evident in 2018–2019. The variation of the freezing period is related to climate anomalies.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121575294","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
Perturbation Algorithm Applied to Sea Surface Temperature Determination 微扰算法在海面温度测定中的应用
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884304
Roberto Alonso, R. Frouin
{"title":"Perturbation Algorithm Applied to Sea Surface Temperature Determination","authors":"Roberto Alonso, R. Frouin","doi":"10.1109/IGARSS46834.2022.9884304","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884304","url":null,"abstract":"The determination of the Sea Surface Temperature (SST), as well as the Land Surface Temperature (LST), is typically achieved through the algorithm called Split Windows (SW). This method is compatible with detectors whose Noise Equivalent Delta Temperature (NEDT) is on the order of 0.1 °K or less. The method increases the observation error by a factor of 4 or 5 on the SST determination. The NEDT of un-cooled micro-bolometers detectors (used in small and medium satellites) is in the range of 0.5°K to 1.5°K. To achieve a SST error less than 0.7°K it is necessary to search for other algorithms. In [1] an alternative method is presented, but in this work a new algorithm is presented, based on the theory of perturbations and stochastic process, compatible with the new generation of areal micro-bolometer detector, that allows one to meet the accuracy requirement.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121667863","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
The SpaceNet 8 Challenge - From Foundation Mapping to Flood Detection SpaceNet 8的挑战——从基础制图到洪水探测
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883741
R. Hänsch, J. Arndt, M. Gibb, Arnold P. Boedihardjo, Tyler Pedelose, Todd M. Bacastow
{"title":"The SpaceNet 8 Challenge - From Foundation Mapping to Flood Detection","authors":"R. Hänsch, J. Arndt, M. Gibb, Arnold P. Boedihardjo, Tyler Pedelose, Todd M. Bacastow","doi":"10.1109/IGARSS46834.2022.9883741","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883741","url":null,"abstract":"Floods are one of the major types of natural disasters responsible for loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Climate change, increasing populations, and urbanisation of flood plains will only increase the risk of flooding in the next few years. SpaceNet 8 presents a dataset that combines building footprint detection, road network extraction, and flood detection covering 850km2, including ~32,000 buildings and ~ 1,300 km of roads, of which ~ 13% and ~ 15% are flooded, respectively.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"35 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113942797","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
Ionospheric Signal Propagation Simulator for Earth Observation Missions 地球观测任务电离层信号传播模拟器
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883781
E. Fernández-Niño, C. Molina, Adriano Camps
{"title":"Ionospheric Signal Propagation Simulator for Earth Observation Missions","authors":"E. Fernández-Niño, C. Molina, Adriano Camps","doi":"10.1109/IGARSS46834.2022.9883781","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883781","url":null,"abstract":"Following the discovery of the ionosphere by Marconi in 1901, different disciplines have been influenced from the ionospheric effects on radio-waves, such as communications or Earth Observation missions. The ionosphere acts as an electrical layer that is continuously changing due mainly to solar activity. Therefore, it is not a trivial work to predict how radio signals would be affected. This study presents the implementation of a Matlab ray-tracer to predict radio-wave propagation through the ionosphere. This program is inspired on a Fortran code developed in the 70's, but it is extended to include the state-of-the-art models, such as the IRI (International Reference Ionosphere), the IGRF (International Geomagnetic Reference Field), and the NRLMSISE-00 (Naval Research Lab, atmospheric model). A statistical model of bubbles and depletions is also included for increased accuracy. The simulator provides several graphs and a text document, both summarizing the ray trajectories and main propagations effects. This tool is being developed as part of an ESA project devoted to the study of ionospheric effects in low frequency radars, namely radar sounders and Synthetic Aperture Radars, and GNSS systems.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"12 S1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113970798","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
Estimating PM2.5 and PM10 on Zhuhai-1 Hyperspectral Imagery 珠海一号高光谱影像的PM2.5和PM10估算
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884493
Shengjie Liu, Q. Shi
{"title":"Estimating PM2.5 and PM10 on Zhuhai-1 Hyperspectral Imagery","authors":"Shengjie Liu, Q. Shi","doi":"10.1109/IGARSS46834.2022.9884493","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884493","url":null,"abstract":"Particulate matter (PM), such as PM2.5 and PM10, was the major pollutant in a severe air pollution episode in 2013 eastern China. Limited by the coverage of stations, fine-scale monitoring at every corner in the city is difficult, if not impossible. Hyperspectral imagery can capture the ground and air information, from which we can estimate the concentrations of PM. In this study, we develop a multitask learning method to estimate the concentrations of PM based on the 10-m hyperspectral data from the newly-launched Zhuhai-1 satellites. We first convert the raw radiance to top-of-atmosphere (TOA) reflectance using the 1985 Wehrli solar irradiance spectrum. Then, we train a multitask network to simultaneously estimate PM2.5 and PM10 concentrations based on the TOA hyperspectral data. Results show that our method leads to estimations of an R-squared of 0.77 for PM2.5 and an R-squared of 0.42 for PM10.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"115 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114003065","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
“Missing-As-Complete” (MAC) Strategy and Hybrid Loss Guided Network Training for Seismic Data Reconstruction “完全缺失”(MAC)策略和混合损失引导网络训练的地震数据重建
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883163
Xue Fei, Yongchang Hui, Bangyu Wu, Rongwei Wang, Ximeng Lian
{"title":"“Missing-As-Complete” (MAC) Strategy and Hybrid Loss Guided Network Training for Seismic Data Reconstruction","authors":"Xue Fei, Yongchang Hui, Bangyu Wu, Rongwei Wang, Ximeng Lian","doi":"10.1109/IGARSS46834.2022.9883163","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883163","url":null,"abstract":"Missing trace reconstruction is a basic step in seismic data processing workflow. Recently, many deep learning based seismic data reconstruction methods have been proposed. However, lack of label data can impair the performance for practical applications due to domain gaps on seismic data prior. In this research, we propose a “Missing-As-Complete” (MAC) strategy for training networks to solve the problem of missing labels in practical situation. Specially, the missing seismic data is directly taken as the “complete” target. While the input is seismic data consisted of missing trace and a second missing trace mask. A hybrid loss function FFL+SSIM +L1 based on the focal frequency loss (FFL), structural similarity (SSIM) and L1 norm is used to further improve the reconstruction performance. Experiments on synthetic data demonstrate that the network can reconstruct reasonable results by the proposed method.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"4 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114028962","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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