IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium最新文献_第4页

An Optimization Algorithm of Moving Targets Refocusing Via Parameter Estimation Dependence of Maximum Sharpness Principle After BP Integral 基于BP积分后最大锐度原理参数估计的运动目标再聚焦优化算法

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324306

Xuyao Tong, M. Xing, Guangcai Sun

引用次数: 0

Vegetation Indices Derived from FengYun-3D MERSI-II Data 基于风云- 3d MERSI-II数据的植被指数

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324123

Xiuzhen Han, F. Weng, Yang Han, He Huang, Shengqi Li

引用次数: 0

Strong Potential for the Detection of Refrozen Ice Layers in Greenland's Firn by Airborne Radar Sounding 机载雷达探测格陵兰冰原再冻层的强大潜力

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324268

R. Culberg, D. Schroeder

{"title":"Strong Potential for the Detection of Refrozen Ice Layers in Greenland's Firn by Airborne Radar Sounding","authors":"R. Culberg, D. Schroeder","doi":"10.1109/IGARSS39084.2020.9324268","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324268","url":null,"abstract":"The formation of impermeable ice layers within Greenland's firn can significantly increase surface meltwater runoff by capping percolation, thus increasing the continent's immediate contributions to sea level rise. Detection of these layers by airborne radar sounding would permit large scale assessment of their spatial coverage and temporal evolution. We present an electromagnetic forward model for radar scattering in dry firn, as well as a statistical model of firn density profiles, which together allow us to robustly simulate airborne radar sounding measurements in the ice sheet near-surface. We use these models to simulate the response of the University of Kansas Accumulation Radar to refrozen ice layers thinner than the radar vertical resolution. Our results suggest that continuous ice layers are detectable as anomalously bright reflections within the firn, so long as the background density does not exceed 0.72 g/cm3. We find that approximately 81% of single ice layers thicker than 2 cm are detectable, as well as over 90% of all multi-layer configurations. This suggests that the Accumulation Radar is an effective tool for studying the spatial and temporal coverage of thin ice layers in Greenland's firn, but such a survey would likely still underestimate the total areal extent.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125320459","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: 6

A Lightweight Convolutional Neural Network for Bitemporal Image Change Detection 基于轻量级卷积神经网络的双时图像变化检测

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323964

Rongfang Wang, Fan Ding, Jiawei Chen, L. Jiao, Liang Wang

引用次数: 5

Sentinel-1 Imagery Incorporating Machine Learning for Dryland Salinity Monitoring: A Case Study in Esperance, Western Australia 结合机器学习的Sentinel-1图像用于旱地盐度监测:以西澳大利亚州埃斯佩兰斯为例

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323426

Qianqian Zhang, Zheng-Shu Zhou, P. Caccetta, J. Simons, Li Li

引用次数: 1

D-SHIELD: DISTRIBUTED SPACECRAFT WITH HEURISTIC INTELLIGENCE TO ENABLE LOGISTICAL DECISIONS D-shield:具有启发式智能的分布式航天器，可实现后勤决策

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323248

S. Nag, M. Moghaddam, Daniel Selva, J. Frank, V. Ravindra, R. Levinson, Amir Azemati, Alan Aguilar, Alan S. Li, R. Akbar

引用次数: 11

Spatial-Spectral Smooth Graph Convolutional Network for Multispectral Point Cloud Classification 多光谱点云分类的空间-光谱光滑图卷积网络

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324584

Qingwang Wang, Xiangrong Zhang, Yanfeng Gu

{"title":"Spatial-Spectral Smooth Graph Convolutional Network for Multispectral Point Cloud Classification","authors":"Qingwang Wang, Xiangrong Zhang, Yanfeng Gu","doi":"10.1109/IGARSS39084.2020.9324584","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324584","url":null,"abstract":"Multispectral point cloud, as a new type of data containing both spectrum and spatial geometry, opens the door to three-dimensional (3D) land cover classification at a finer scale. In this paper, we model the multispectral point cloud as a spatial-spectral graph and propose a smooth graph convolutional network for multispectral point cloud classification, abbreviated 3SGCN. We construct the spectral graph and spatial graph respectively to mine patterns in spectral and spatial geometric domains. Then, the multispectral point cloud graph is generated by combining the spatial and spectral graphs. For remote sensing scene classification tasks, it is usually desirable to make the classification map relatively smooth and avoid salt and pepper noise. Heat operator is introduced to enhance the low- frequency filters and enforce the smoothness in the graph signal. Further, a graph -based smoothness prior is deployed in our loss function. Experiments are conducted on real multispectral point cloud. The experimental results demonstrate that 3 SGCN can achieve significant improvements in comparison with several state-of-the art algori thms.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672002","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

Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning 基于深度学习的航空图像绘制输电线路基础设施

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323851

Wei Hu, Ben Alexander, Wendell Cathcart, Atsushi Hu, Varun Nair, Lin Zuo, Jordan M. Malof, L. Collins, Kyle Bradbury

{"title":"Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning","authors":"Wei Hu, Ben Alexander, Wendell Cathcart, Atsushi Hu, Varun Nair, Lin Zuo, Jordan M. Malof, L. Collins, Kyle Bradbury","doi":"10.1109/IGARSS39084.2020.9323851","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323851","url":null,"abstract":"Access to electricity positively correlates with many beneficial socioeconomic outcomes in the developing world including improvements in education, health, and poverty. Efficient planning for electricity access requires information on the location of existing electric transmission and distribution infrastructure; however, the data on existing infrastructure is often unavailable or expensive. We propose a deep learning based method to automatically detect electric transmission infrastructure from aerial imagery and quantify those results with traditional object detection performance metrics. In addition, we explore two challenges to applying these techniques at scale: (1) how models trained on particular geographies generalize to other locations and (2) how the spatial resolution of imagery impacts infrastructure detection accuracy. Our approach results in object detection performance with an F1 score of 0.53 (0.47 precision and 0.60 recall). Using training data that includes more diverse geographies improves performance across the 4 geographies that we examined. Image resolution significantly impacts object detection performance and decreases precipitously as the image resolution decreases.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125682452","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

Classification of Wheat and Barley Fields Using Sentinel-1 Backscatter 基于Sentinel-1反向散射的小麦和大麦田分类

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323560

I. Pfeil, F. Reuß, M. Vreugdenhil, C. Navacchi, W. Wagner

引用次数: 2

Detecting Layer Height of Smoke and Dust Aerosols Over Vegetated Land and Water Surfaces via Oxygen Absorption Bands 利用氧吸收带探测植被覆盖的陆地和水面上烟尘气溶胶的层高

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323130

J. Zeng, Xiaoguang Xu, Jun Wang, Yi Wang, Xi Chen, Zhen Lu, O. Torres, J. Reid, S. Miller

引用次数: 0