IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium最新文献_第2页

A Focusing Algorithm Based on CSA for Middle-Earth-Orbit SAR Combined with Two-Step Azimuth Compensation 结合两步方位补偿的中轨道SAR CSA调焦算法

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884731

Wanling Liao, Bin Zhao, Xin Zhu, Yun Zhang

引用次数: 0

A Method for Estimating Phase Error Between DBF SAR Elevation Channels Based on Ground Control Points 基于地面控制点的DBF SAR高程信道相位误差估计方法

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883886

H. Chen, Feng Ming, Liang Li, Guikun Liu

引用次数: 0

Development of an Approach for Early Weed Detection with UAV Imagery 基于无人机图像的早期杂草检测方法研究

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883564

V. Singh, Dharmendra Singh

{"title":"Development of an Approach for Early Weed Detection with UAV Imagery","authors":"V. Singh, Dharmendra Singh","doi":"10.1109/IGARSS46834.2022.9883564","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883564","url":null,"abstract":"Curating a precise decision-based classifier algorithm to automate target detection based on feature extraction(s) in UAV imagery can assist in various scientific and practical applications. Localization and detection of weed in sugarcane field is a critical classification problem. Vegetative stage of weed, especially, when it is growing and is at its earliest phase, exhibits challenging characteristics such as small weed patch area and color merging tendencies with the crop, which makes it a very typical task to correctly identify, localize and detect weed. A meticulous and scientific detection of weed at early stages may aid in providing timely and quick treatment in the scene to preserve crop health. Random forest classifier is a combination of numerous decision tree classifiers and is a type of ensemble learning which has ample potential for clustering data of similar nature into different classes. This predictive averaging approach has the capability to detect early weed patches, which in turn facilitates precision agriculture. The presented research focusses on binary classification of UAV data of weed infested sugarcane field using decision based random forest classifier at weed's premature stage. Small and multiple green on green weed patches in sugarcane field have been accurately detected and classified into two classes “weed” and “crop”. This algorithm helps detect early weed patches in agricultural setting which in turn aids in weed removal strategies.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"46 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":"121252427","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

SAR Azimuth Low Sidelobe Window Function Design SAR方位低旁瓣窗函数设计

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884901

Youshan Tan, Lei Wang, Hongyang An, Min Li, Mingyue Lou, Zhongyu Li, Junjie Wu, Jianyu Yang

引用次数: 0

Multipass InSAR with Multiple Bands: Application to Landslides Mapping and Monitoring 多波段多通道InSAR在滑坡测绘与监测中的应用

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883733

D. Reale, S. Verde, F. Calà, P. Imperatore, A. Pauciullo, A. Pepe, V. Zamparelli, Eugenio Sansosti, G. Fornaro

引用次数: 2

ALMOND ORCHARD MANAGEMENT USING MULTI-TEMPORAL UAV DATA: A PROOF OF CONCEPT 杏仁园管理使用多时无人机数据:一个概念的证明

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883370

Nathalie Guimarães, L. Pádua, J. Sousa, Albino Bento, P. Couto

引用次数: 0

Satellite Data-Driven Deep Learning Approach for Monitoring Groundwater Drought in South Korea 卫星数据驱动的深度学习方法监测韩国地下水干旱

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884120

J. Seo, Sang-Il Lee

{"title":"Satellite Data-Driven Deep Learning Approach for Monitoring Groundwater Drought in South Korea","authors":"J. Seo, Sang-Il Lee","doi":"10.1109/IGARSS46834.2022.9884120","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884120","url":null,"abstract":"Due to the effect of climate change on the hydrological cycle process, the severity and frequency of drought have increased. Typically, drought begins with meteorological drought, after which it propagates to agricultural and hydrological drought. Thus, it is essential to investigate the process involved in the drought propagation from meteorological to groundwater drought. In this study, we investigated groundwater drought by calculating the standardized groundwater level index (SGI) using predicted groundwater storage changes (GWSC) based on satellite data-driven deep learning models. The GWSC was predicted using two deep learning models (the convolution neural network-long short term memory (CNN-LSTM) and LSTM), and the results were validated using in situ observation data. In addition, the SGI was compared to meteorological, agricultural, and hydrological drought indices based on remote sensed data, and the drought propagation was analyzed. This study revealed the potential of satellite data-driven deep learning models for assessing groundwater droughts, which is important for the development of multi-scale drought monitoring systems.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"28 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":"127296954","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

GRE and Beyond: A Global Road Extraction Dataset GRE及以后:一个全球道路提取数据集

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9883915

Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng, Dingyuan Chen

{"title":"GRE and Beyond: A Global Road Extraction Dataset","authors":"Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng, Dingyuan Chen","doi":"10.1109/IGARSS46834.2022.9883915","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9883915","url":null,"abstract":"Accurate and timely road mapping that describes the road network geometry and topology is the key element of intelligent transport systems and smart city management. However, current global road maps like OpenStreetMap (OSM) are typically outdated and spatially incomplete with uneven accuracies. Although the development of remote sensing satellite technology and the advance of computer vision technology have made it possible to quickly extract road networks from massive very-high-resolution (VHR) remote sensing imagery, existing road extraction methods are limited by the problem: lacking of an accurate and diverse training dataset for global-scale road extraction, and manually labelling millions of road samples for training a global model is labor intensive. To address this problem, we utilized VHR satellite imagery and open-source crowdsourcing geospatial big data to build a robust global-scale road training dataset, termed GlobalRoadNet, for global road extraction (GRE) and beyond. The proposed GlobalRoadNet contains 47210 samples from 121 capital cities of six continents in Europe, Africa, Asia, South America, Oceania, and North America. Experimental results show that GlobalRoadNet can significantly improve model performance, not only can be applied for road extraction, but also has the potential to update OSM road data.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"16 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":"127539948","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

Characterizing Biophysical Attributes in Tropical Secondary Forest Environments with Multitemporal Hyperspectral Images 利用多时相高光谱图像表征热带次生林环境的生物物理属性

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884914

V. Liesenberg

引用次数: 0

On the De-Ramping of SLC-IW Tops SAR Data and Ocean Circulation Parameters Estimation SLC-IW Tops SAR数据的消斜与海洋环流参数估算

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2022-07-17 DOI: 10.1109/IGARSS46834.2022.9884331

M. Iqbal, A. Anghel, M. Datcu

{"title":"On the De-Ramping of SLC-IW Tops SAR Data and Ocean Circulation Parameters Estimation","authors":"M. Iqbal, A. Anghel, M. Datcu","doi":"10.1109/IGARSS46834.2022.9884331","DOIUrl":"https://doi.org/10.1109/IGARSS46834.2022.9884331","url":null,"abstract":"The spectral characteristics of single-look complex - inter-ferometric wide (SLC-IW) swath, terrain observation by progressive scan (TOPS), are significantly different from those of strip-map (SM). Due to the burst mode and series of sub-swaths, the target area is scanned for a short period of time. Therefore, swath width comes at the expense of azimuth resolution. To eliminate quadratic phase drift and achieve SLC baseband, significant processing is required. De-ramping is a necessary step to compute ocean circulation parameters. In this work, we extract ocean parameters from the complex echo signal based on data driven Doppler centroid $(f_{DC})$ regardless of the OCN product information and geophysical $f_{DC}$ image. The radial surface velocity (RSV) is retrieved from Doppler history, and the significant wave height (SWH) is estimated with an empirical relationship of RSV. The results of ocean circulation parameters are promising when compared with benchmark and in-situ data. This work demonstrates the efficacy and necessity of de-ramping the TOPS data for subsequent use in a variety of ocean remote sensing applications.","PeriodicalId":426003,"journal":{"name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","volume":"14 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":"124837293","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