IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium最新文献_第3页

Analytics Center Framework for Estimating the Circulation and Climate of the Ocean 估算海洋环流和气候的分析中心框架

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8897904

Thomas Huang, Maya DeBellis, I. Fenty, P. Heimbach, J. Jacob, O. Wang, Elizabeth Yam

引用次数: 0

Features Extraction of the Doppler Frequency Signature of a Human Walking at 1 GHz 人体行走1ghz多普勒频率特征提取

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8897817

G. Manfredi, J. Ovarlez, L. Thirion-Lefevre

引用次数: 7

Sea Surface Salinity Retrievals from Aquarius Using Neural Networks 利用神经网络反演水瓶座海面盐度

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8898959

Y. Soldo, D. Vine, E. Dinnat

{"title":"Sea Surface Salinity Retrievals from Aquarius Using Neural Networks","authors":"Y. Soldo, D. Vine, E. Dinnat","doi":"10.1109/IGARSS.2019.8898959","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898959","url":null,"abstract":"Even though the Sea Surface Salinity (SSS) retrieved from Aquarius are generally very close to in-situ measurements, the level of similarity varies with the region and with the circumstances of the observations (wind speed, sea surface temperature, etc.). SSS is currently retrieved from the brightness temperatures measured by Aquarius and applying the current theoretical model for the propagation and emission of the natural thermal radiation. In this contribution we consider an alternative retrieval approach based on a Neural Network (NN) with the goal of improving the subsets of Aquarius SSS data that are in poorer agreement with in-situ measurements. The subset considered here are the SSS retrieved at latitudes higher than 30˚. The output of the NN approach are compared against in-situ measurements using four statistical metrics (correlation coefficient, bias, RMSD and 5% trimmed range). The output of the NN and the nominal Aquarius SSS are compared against SSS values from in-situ measurements and from ocean models. From these comparisons it appears that the output of the NN matches the in-situ measurements better than the nominal Aquarius SSS.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"8143-8146"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78573261","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

UAVSAR Real-Time Embedded GPU Processor UAVSAR实时嵌入式GPU处理器

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8900055

B. Hawkins, W. Tung

引用次数: 3

Analysis of L Band Radar Data Over Tropical Agricultural Areas 热带农业区L波段雷达数据分析

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8899079

M. Zribi, M. Sekhar, S. Bandyopadhyay, S. Bousbih, A. Al Bitar, S. K. Tomer, N. Baghdadi

引用次数: 0

Deep Learning for Semantic Segmentation of UAV Videos 基于深度学习的无人机视频语义分割

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8899786

Yiwen Wang, Ye Lyu, Yanpeng Cao, M. Yang

引用次数: 6

Bathysent - A Method to Retrieve Coastal Bathymetry from Sentinel-2 从Sentinel-2检索海岸测深数据的一种方法

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8898940

D. Raucoules, M. Michele, D. Idier, F. Smaï, M. Foumelis, F. Boulahya, E. Volden, V. Drakopoulou, Przemysław Mujta

引用次数: 2

Sentinel-1 and Sentinel-2 Data for Soil Moisture and Irrigation Mapping Over Semi-Arid Region 基于Sentinel-1和Sentinel-2数据的半干旱区土壤水分灌溉制图

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8897883

S. Bousbih, M. Zribi, M. El-Hajj, N. Baghdadi, Z. Lili-Chabaane, P. Fanise, G. Boulet

{"title":"Sentinel-1 and Sentinel-2 Data for Soil Moisture and Irrigation Mapping Over Semi-Arid Region","authors":"S. Bousbih, M. Zribi, M. El-Hajj, N. Baghdadi, Z. Lili-Chabaane, P. Fanise, G. Boulet","doi":"10.1109/IGARSS.2019.8897883","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897883","url":null,"abstract":"Identifying the irrigated areas is essential for waters managers who are in charge of distributing this resource over a large scale. The monitoring of water soil content and irrigation is a powerful tool for water resource management. The potential of Sentinel-1 (S1) and Sentinel-2 (S2) data for estimating the soil moisture and irrigation is studied over covered surfaces. An inversion algorithm of the Water Cloud Model (WCM) was developed after calibrating and validating the model over the Kairouan plain, a semi-arid region in Tunisia. The aim is to restitute soil moisture over the whole region. The developed algorithm used a synergy between S1, radar data in VV polarization, and NDVI derived from S2 optical data at high spatial resolution. The results showed good accuracy between retrieved and measured soil moisture with a Root Mean Square Error (RMSE) lower than 6 vol.%. Then, the resulting soil moisture maps were used for irrigation mapping. The process used a combination of Support Vector Machine (SVM) and Decision Tree classifications to distinguish between irrigated and non-irrigated agricultural fields. Results from the annual irrigation map show that the overall accuracy on the classification is about 77%.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"6 1","pages":"7022-7025"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78239450","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

On the Assimilation of CFOSAT Wave Data in the Wave Model MFWAM : Verification Phase 波浪模型MFWAM中CFOSAT波浪数据的同化:验证阶段

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8900180

L. Aouf, A. Dalphinet, D. Hauser, L. Delaye, C. Tison, B. Chapron, Laura Hermozo, C. Tourain

引用次数: 9

Oil Slick Volume Estimation from Combined Use of Airborne Hyperspectral and Pool Experiment Data 基于机载高光谱和水池实验数据的浮油体积估算

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2019-07-28 DOI: 10.1109/IGARSS.2019.8899057

Roupioz Laure, Viallefont-Robinet Françoise, Miegebielle Véronique

{"title":"Oil Slick Volume Estimation from Combined Use of Airborne Hyperspectral and Pool Experiment Data","authors":"Roupioz Laure, Viallefont-Robinet Françoise, Miegebielle Véronique","doi":"10.1109/IGARSS.2019.8899057","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899057","url":null,"abstract":"To date, estimating oil thickness on the sea surface remains a challenge in most cases. When oil thickness estimation using optical data is limited by the absorption properties of the target, a solution consists in combining experimental and airborne hyperspectral data. We developed a method to identify thickness classes from hyperspectral data which, combined with realistic thickness values derived from a pool experiment, allows to estimate slick volume. Hyperspectral images of the same oil emulsion were acquired over a pool and at sea, under real conditions. From the pool data, we derived two classes: the thin and the thick pixels, along with their respective thickness. These classes are then identified on the airborne images acquired during the NOFO campaign by generating a detection mask and using two classification approaches based on spectral indices. The proposed method allows to correctly identify the two thickness classes and, combined with the data from the pool experiment, provides a total slick volume larger than the one derived for the Bonn Agreement Oil Appearance Code.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"73 1","pages":"5776-5779"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72647775","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