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
A Generic Storage Method for Coherent Scatterers and Their Contextual Attributes 相干散射子及其上下文属性的通用存储方法
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553453
Marc F.D. Bruna, F. J. Leijen, R. Hanssen
{"title":"A Generic Storage Method for Coherent Scatterers and Their Contextual Attributes","authors":"Marc F.D. Bruna, F. J. Leijen, R. Hanssen","doi":"10.1109/IGARSS47720.2021.9553453","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553453","url":null,"abstract":"In radar interferometry, the method of storage for coherent scatterers and their attributes directly influences the ability for interpretation. Especially with the complexity and ambiguity of InSAR observations, the need for a consistent and queryable spatial data-platform becomes relevant. Our proposed method uses the concept of a spacetime matrix to store the coherent scatterers, implemented by means of a spatial database. The stored InSAR data are partitioned in modules of the displacement time series, inherent scatterer and processing-related attributes, and their corresponding contextual attributes. Data and context-driven queries are facilitated by the use of spatial indices. The method is illustrated by a case study for building stability in the northwestern part of Rotterdam, the Netherlands. The contextual attributes used characterize building foundations in part of the city.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"4 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":"129696845","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
Robust Deep Metric Learning for Remote Sensing Images with Noisy Annotations 基于噪声标注的遥感图像鲁棒深度度量学习
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554696
Jian Kang, R. Fernández-Beltran, Puhong Duan, Xudong Kang, A. Plaza
{"title":"Robust Deep Metric Learning for Remote Sensing Images with Noisy Annotations","authors":"Jian Kang, R. Fernández-Beltran, Puhong Duan, Xudong Kang, A. Plaza","doi":"10.1109/IGARSS47720.2021.9554696","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554696","url":null,"abstract":"Manual and automatic annotation of Remote Sensing (RS) scenes are rather complex tasks which may unavoidably introduce some degree of mislabeled data in large-scale archives. In this regard, noisy annotations become an important constraint for deep metric learning-based RS characterization methods since most of them are trained in a supervised way. To address this problem, here we investigate the use of deep metric learning for characterizing RS scenes with noisy labels. Specifically, we consider the Normalized Softmax Loss and develop a robust extension, i.e., the Robust Normalized Softmax Loss (RNSL), in order to effectively capture the semantic relationships among RS scenes with mislabeled ground-truth information. The conducted experiments, using the K-NN classifier and two benchmark RS image archives, show the potential of the proposed approach with respect to other state-of-the-art methods.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"35 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":"129852512","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
DO-UNet, DO-LinkNet: UNet, D-LinkNet with DO-Conv for the Detection of Settlements without Electricity Challenge DO-UNet, DO-LinkNet:带DO-Conv的UNet, D-LinkNet用于无电定居点检测
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553097
Ruoxian Feng, Mengjiao Wang, Xuanming Zhang, Jun Zhang, L. Jiao, Xu Liu, Fang Liu
{"title":"DO-UNet, DO-LinkNet: UNet, D-LinkNet with DO-Conv for the Detection of Settlements without Electricity Challenge","authors":"Ruoxian Feng, Mengjiao Wang, Xuanming Zhang, Jun Zhang, L. Jiao, Xu Liu, Fang Liu","doi":"10.1109/IGARSS47720.2021.9553097","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553097","url":null,"abstract":"In this paper, two semantic segmentation models, DO-UNet and DO-LinkNet, are presented for the detection of human settlements, and a threshold-based model is proposed to detect areas with electricity. In DO-UNet and DO-LinkNet, the conventional convolutional layer is replaced with depthwise over-parameterized convolutional layer. Also, an extra pooling operation is carried out in the last layer since the size of the input images is different from that of the labels. Depthwise over-parameterized convolutional layer enhances the convolutional layer with an additional depthwise convolution. Pooling operation can accelerate training speed, increase the receptive field in feature extraction, and reduce the requirement of network complexity. In the detection of settlements without electricity challenge track, our best F1-score on the validation set and the test set are 0.8820 and 0.8798, respectively.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"51 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":"129886079","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
Integrated Photonics Technology for Earth Science Remote-Sensing Lidar 地学遥感激光雷达集成光子学技术
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553656
F. Sang, Joseph Fridlander, Victoria Rosborough, S. Brunelli, L. Coldren, J. Klamkin, Jeffrey R. Chen, K. Numata, R. Kawa, M. Stephen
{"title":"Integrated Photonics Technology for Earth Science Remote-Sensing Lidar","authors":"F. Sang, Joseph Fridlander, Victoria Rosborough, S. Brunelli, L. Coldren, J. Klamkin, Jeffrey R. Chen, K. Numata, R. Kawa, M. Stephen","doi":"10.1109/IGARSS47720.2021.9553656","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553656","url":null,"abstract":"We present recent progress on a photonic integrated lidar system for carbon dioxide (CO2) active remote sensing at 1572.335 nm. With integration, the cost, size, weight and power (CSWaP) of the system are significantly improved. System and subsystem level results are demonstrated.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"46 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":"126683298","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
Style Transformation-Based Change Detection Using Adversarial Learning with Object Boundary Constraints 基于风格变换的对象边界约束对抗学习变化检测
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554645
Xiaokang Zhang, Weikang Yu, Man-On Pun, Ming Liu
{"title":"Style Transformation-Based Change Detection Using Adversarial Learning with Object Boundary Constraints","authors":"Xiaokang Zhang, Weikang Yu, Man-On Pun, Ming Liu","doi":"10.1109/IGARSS47720.2021.9554645","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554645","url":null,"abstract":"Deep learning has shown promising results on change detection (CD) from bi-temporal remote sensing imagery in recent years. However, it still remains challenging to cope with the pseudo-changes caused by seasonal differences and style variations of bi-temporal images. In this paper, an object-level boundary-preserving generative adversarial network (BPGAN) is developed for style transformation-based CD of bi-temporal images. To achieve this purpose, image objects derived in the spectral domain are incorporated into the image translation to generate object-level target-style-like images. In particular, constraints on object boundary consistency and object homogeneity are established in the adversarial learning to maintain the style and content consistency while regularizing the network training. Furthermore, the Superpixel-Based Fast Fuzzy c-Means (SF-FCM) algorithm is utilized for efficient CD from the object-level style-transformed images. Extensive experiments on SPOT5 and GF1 data confirm the effectiveness of the proposed approach.","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":"126796028","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
Analysis of Snow Coherence Conservation for SWE Retrieval at L-, S-, C-and X-Bands L-、S-、c -和x波段积雪相干保持分析
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554103
J. J. Ruiz, J. Lemmetyinen, A. Kontu, Riku Tarvainen, J. Pulliainen, Risto Vehmas, J. Praks
{"title":"Analysis of Snow Coherence Conservation for SWE Retrieval at L-, S-, C-and X-Bands","authors":"J. J. Ruiz, J. Lemmetyinen, A. Kontu, Riku Tarvainen, J. Pulliainen, Risto Vehmas, J. Praks","doi":"10.1109/IGARSS47720.2021.9554103","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554103","url":null,"abstract":"Accurate measurement of Snow Water Equivalent (SWE) from remote sensors is still an on-going topic with many open challenges. Interferometric Synthetic Aperture Radars (InSAR) offers the possibility of retrieving SWE changes between acquisitions by exploiting the relation between the interferometric phase, the snow depth and the water contained on it. However, it is susceptible to estimation errors due to loss of coherence. Sources of decorrelation in snow have not been exhaustively investigated. Here we present the results from the SWE retrieval during both the 2019–2020 and 2020–2021 winters and an analysis of various environmental parameters on the snow coherence using SodSAR (Sodankylä SAR). SodSAR is a 1-10GHz tower-based fully polarimetric SAR with InSAR capabilities operating in northern Finland. Acquisitions were made every 12 hours for the 2019–2020 winter and every 6 hours for the 2020–2021 winter. In-situ instruments are used for validation and comparison.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"7 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":"126838667","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
Remote Sensing of Deep Snow With C Band Radar Data: Volume and Surface Scattering C波段雷达数据遥感深雪:体积和表面散射
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554866
Jiyue Zhu, L. Tsang, T. Liao
{"title":"Remote Sensing of Deep Snow With C Band Radar Data: Volume and Surface Scattering","authors":"Jiyue Zhu, L. Tsang, T. Liao","doi":"10.1109/IGARSS47720.2021.9554866","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554866","url":null,"abstract":"The capability of Sentinel 1 C band radar observations for mapping snow depth or snow water equivalent (SWE) has been demonstrated recently. However, theoretical models of C band radar signatures for snow retrieval are still lacking. In this paper, we study the volume scattering of snowpack and the surface scattering from the snow/soil interface. The snowpack is computer generated including dense ice aggregates. The volume scattering is calculated by the dense media radiative transfer (DMRT) model and the surface scattering is computed with the Oh model. With well characterized surface scattering, surface scattering contributions can be subtracted from radar observations to enhance sensitivity of volume scattering to SWE. The study will help improve C band SWE retrieval and provide the theoretical basis for retrieval algorithms.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"232 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":"126888624","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
New Insights into a Rock-Related TIR Anomaly on the Moon from CE-2 Celms Satellite Data 从CE-2 Celms卫星数据对月球岩石相关TIR异常的新见解
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554614
Z. Meng, Hengxi Liu, Wen-Yen Chang, Z. Cai, Tianqi Tang, Yanxiang Shi, Yongchun Zheng
{"title":"New Insights into a Rock-Related TIR Anomaly on the Moon from CE-2 Celms Satellite Data","authors":"Z. Meng, Hengxi Liu, Wen-Yen Chang, Z. Cai, Tianqi Tang, Yanxiang Shi, Yongchun Zheng","doi":"10.1109/IGARSS47720.2021.9554614","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554614","url":null,"abstract":"The cause of the hot anomaly is one of the important scientific aims of the current lunar studies. In this study, aimed at a rock-related hot anomaly revealed by the Diviner thermal infrared data, the brightness temperature (TB) derived from the Chang'e-2 microwave sounder (CELMS) data is employed to evaluate its thermophysical features of the regolith. According to the TB performances, the ilmenite-rich material is identified in the region. Thus, we denied the rock influence and ascribed the region as a potential cryptomare. This study provides a new way to evaluate the thermophysical features of the lunar regolith with the CELMS data.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"12 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":"127005488","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 Comprehensive Emission Model for Layered Inhomogeneous Medium with Application to Passive Remote Sensing of Snow and Ice Layers 层状非均匀介质综合发射模型及其在冰雪被动遥感中的应用
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9554703
Dongjin Bai, Xiaolong Dong, S. Tjuatja, Di Zhu
{"title":"A Comprehensive Emission Model for Layered Inhomogeneous Medium with Application to Passive Remote Sensing of Snow and Ice Layers","authors":"Dongjin Bai, Xiaolong Dong, S. Tjuatja, Di Zhu","doi":"10.1109/IGARSS47720.2021.9554703","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9554703","url":null,"abstract":"Microwave emission from layered inhomogeneous medium is affected by both incoherent scattering and coherent interaction within the layer. Reported emission observations of snow-covered surface and ice sheet indicate that effects of layer boundary interference are more prominent at low frequencies. This paper presents a comprehensive layer emission model (CLEM) for layered inhomogeneous medium that fully accounts for incoherent scattering and coherent boundary interactions within the layer. The CLEM model is based on scattering operator (matrix doubling) formulation, which provides the basic framework for integrating rough boundary scattering, volume scattering, and coherent multiple reflections at layer boundaries. Coherent boundary interactions are accounted for through novel integrated wave- and intensity-based layer scattering operators. Initial model validation using published Elbara-II data and SodRad data shows good agreement between CLEM predictions and measured results.","PeriodicalId":315312,"journal":{"name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","volume":"16 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":"127038984","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
LiDAR-Aided Total Variation Regularized Nonnegative Tensor Factorization for Hyperspectral Unmixing 激光雷达辅助全变分正则化非负张量分解高光谱解混
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Pub Date : 2021-07-11 DOI: 10.1109/IGARSS47720.2021.9553137
Atakan Kaya, Kubilay Atas, S. Kahraman
{"title":"LiDAR-Aided Total Variation Regularized Nonnegative Tensor Factorization for Hyperspectral Unmixing","authors":"Atakan Kaya, Kubilay Atas, S. Kahraman","doi":"10.1109/IGARSS47720.2021.9553137","DOIUrl":"https://doi.org/10.1109/IGARSS47720.2021.9553137","url":null,"abstract":"Hyperspectral unmixing (HU) is an important research field in hyperspectral image processing. In recent years, Nonnegative Tensor Factorization (NTF)-based methods have gained great importance in remote sensing imagery, especially hyperspectral unmixing, regardless of any information loss. Nevertheless, NTF has some disadvantages, such as signal-to-noise ratio (SNR) and noncovexity conditions. Mentioned problem can be solved by introducing some spatial regularizations. On the other hand, LiDAR data provides Digital Surface Model (DSM) information gives accurate elevation information about the observed scene. Moreover, total variation (TV)-based regularization provides piecewise smoothness and it preserve edge structure information in the abundance maps. However, this property could be inappropriate for pixels located in edges. LiDAR-DSM alleviates this problem by contributing neighboring objects pixels differently. In this paper, we proposed a simple yet efficient HU framework that incorporates LiDAR data with TV regularized matrix–vector NTF method (LiMV-NTF-TV). Experimental studies are carried out on simulation data sets and demonstrate that the proposed framework can provide better abundance estimation maps.","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":"130667494","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
首页 上一页 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