2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)最新文献_第10页

PHYSICS-based retrieval of scattering albedo and vegetation optical depth using multi-sensor data integration 基于多传感器数据集成的散射反照率和植被光学深度的物理检索

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-25 DOI: 10.1109/IGARSS.2017.8127958

T. Jagdhuber, M. Baur, M. Link, M. Piles, D. Entekhabi, C. Montzka, Jaakko Seppänen, O. Antropov, J. Praks, A. Loew

引用次数: 1

SAR-based wind fields over offshore wind farms — A valuable tool for planning, monitoring and optimization 基于sar的海上风电场—规划、监测和优化的宝贵工具

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-25 DOI: 10.1109/IGARSS.2017.8127281

S. Jacobsen, A. Pleskachevsky, S. Singha, A. Frost, D. Velotto

引用次数: 0

Cloud removal by fusing multi-source and multi-temporal images 融合多源多时间图像的去云方法

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-25 DOI: 10.1109/IGARSS.2017.8127522

Chengyue Zhang, Zhiwei Li, Qing Cheng, Xinghua Li, Huanfeng Shen

引用次数: 7

Deep residual networks for hyperspectral image classification 高光谱图像分类的深度残差网络

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-25 DOI: 10.1109/IGARSS.2017.8127330

Zilong Zhong, Jonathan Li, Lingfei Ma, Han Jiang, He Zhao

{"title":"Deep residual networks for hyperspectral image classification","authors":"Zilong Zhong, Jonathan Li, Lingfei Ma, Han Jiang, He Zhao","doi":"10.1109/IGARSS.2017.8127330","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127330","url":null,"abstract":"Deep neural networks can learn deep feature representation for hyperspectral image (HSI) interpretation and achieve high classification accuracy in different datasets. However, counterintuitively, the classification performance of deep learning models degrades as their depth increases. Therefore, we add identity mappings to convolutional neural networks for every two convolutional layers to build deep residual networks (ResNets). To study the influence of deep learning model size on HSI classification accuracy, this paper applied ResNets and CNNs with different depth and width using two challenging datasets. Moreover, we tested the effectiveness of batch normalization as a regularization method with different model settings. The experimental results demonstrate that ResNets mitigate the declining-accuracy effect and achieved promising classification performance with 10% and 5% training sample percentages for the University of Pavia and Indian Pines datasets, respectively. In addition, t-Distributed Stochastic Neighbor Embedding (t-SNE) provides a direct view of the extracted features through dimensionality reduction.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"3 4 1","pages":"1824-1827"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90584732","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: 81

Estimation of vegetation loss coefficients and canopy penetration depths from smap radiometer and ICESat lidar data 利用smap辐射计和ICESat激光雷达数据估算植被损失系数和冠层穿透深度

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-24 DOI: 10.1109/igarss.2017.8127602

M. Baur, T. Jagdhuber, M. Link, M. Piles, D. Entekhabi, A. Fink

引用次数: 2

Similarity criterion for SAR tomography over dense urban area 稠密城区SAR层析成像的相似准则

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-24 DOI: 10.1109/IGARSS.2017.8127315

Clement Rambour, L. Denis, F. Tupin, J. Nicolas, H. Oriot, L. Ferro-Famil, C. Deledalle

引用次数: 3

High-resolution enhanced product based on SMAP active-passive approach using Sentinel 1 data and its applications 基于Sentinel 1数据的SMAP主-被动方法的高分辨率增强产品及其应用

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-24 DOI: 10.1109/IGARSS.2017.8127500

N. Das, D. Entekhabi, Seungbum Kim, T. Jagdhuber, R. Dunbar, S. Yueh, A. Colliander

引用次数: 6

Simulating L/L-band and C/L-band active-passive microwave covariation of crops with the Tor Vergata scattering and emission model for a SMAP-Sentinel 1 combination 利用Tor Vergata散射和发射模型模拟作物L/L波段和C/L波段主动式微波共变

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-24 DOI: 10.1109/IGARSS.2017.8127913

M. Link, D. Entekhabi, T. Jagdhuber, P. Ferrazzoli, L. Guerriero, M. Baur, R. Ludwig

{"title":"Simulating L/L-band and C/L-band active-passive microwave covariation of crops with the Tor Vergata scattering and emission model for a SMAP-Sentinel 1 combination","authors":"M. Link, D. Entekhabi, T. Jagdhuber, P. Ferrazzoli, L. Guerriero, M. Baur, R. Ludwig","doi":"10.1109/IGARSS.2017.8127913","DOIUrl":"https://doi.org/10.1109/IGARSS.2017.8127913","url":null,"abstract":"The NASA Soil Moisture Active Passive (SMAP) mission aims to disaggregate L-band microwave brightness temperatures (∼40 km2) with finer resolution radar backscatter (1–3 km2) to obtain an intermediate resolution soil moisture product. The disaggregation is based on a linear functional relationship between backscatter and emissivity microwave observations that is captured by a covariation parameter β. Since SMAP's L-Band radar has stopped operations in July 2015, the substitution of Sentinel 1's C-Band radar for an operational soil moisture product is in preparation. However, while multiple studies have provided understanding of active-passive covariation for the L/L-Band case, little is known about the C/L-Band case. We utilize the Tor Vergata discrete backscatter and emission model to simulate growing wheat and corn stands and calculate the covariation parameter β for the L/L-Band and C/L-Band case. The study aims to provide insights into the strength, temporal dynamics and underlying scattering mechanisms of active-passive covariation for different vegetation types and frequency combinations. Our results indicate that for the C/L-Band case, vegetation cover limitations are generally more severe, and different β-dynamics and underlying scattering mechanisms are observed with respect to the L/L-Band case.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"43 1","pages":"4143-4146"},"PeriodicalIF":0.0,"publicationDate":"2017-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76918631","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

High resolution sea ice drift estimation using combined TerraSAR-X and RADARSAT-2 data: First tests 结合使用TerraSAR-X和RADARSAT-2数据的高分辨率海冰漂移估计:首次试验

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-24 DOI: 10.1109/IGARSS.2017.8126966

A. Frost, S. Jacobsen, S. Singha

引用次数: 5

Research oriented foss solution for automatic oil spill detection using risat-1 sar data 基于risat-1 sar数据的溢油自动检测技术研究

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Pub Date : 2017-07-23 DOI: 10.1109/IGARSS.2017.8127659

Pooja Shah, T. Zaveri, Raj Kumar, S. Sharma, Darshan Patel

引用次数: 1