2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)最新文献_第2页

Experimental Analysis of the Hongqi-1 H9 Satellite Imagery for Geometric Positioning 红旗一号H9卫星图像几何定位实验分析

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358950

Wenping Song, Yang Bai, Xiang Li, Shiqiang Tian, X. Qi

引用次数: 0

InGARSS 2020 Author Index InGARSS 2020作者索引

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/ingarss48198.2020.9358946

引用次数: 0

Estimation of the Green and Blue Water Footprint of Kharif Rice Using Remote Sensing Techniques: a Case Study of Ranchi 利用遥感技术估算水稻绿水和蓝水足迹:以兰契为例

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358924

Swadhina Koley, J. C

引用次数: 1

Shoreline Change in Response to the Construction of a Flood Canal in Jaro, Iloilo City, Philippines 菲律宾伊洛伊洛市Jaro修建泄洪渠后的海岸线变化

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358962

Paul Caesar M. Flores, F. Siringan

{"title":"Shoreline Change in Response to the Construction of a Flood Canal in Jaro, Iloilo City, Philippines","authors":"Paul Caesar M. Flores, F. Siringan","doi":"10.1109/InGARSS48198.2020.9358962","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358962","url":null,"abstract":"Understanding shoreline change due to engineering modifications and how it compares with the long-term trends is vital for future development plans. In this study, we focused on the Jaro Floodway, which was constructed in 2012 to mitigate the yearly floods experienced by Iloilo City. Shoreline positions were extracted from geometrically corrected historical maps (1947, 1955, and 1988) and Landsat images (1998, 2004, 2006, 2008, 2010, 2014, 2016, and 2018) for change analysis. Between 1947-1988, the coastline prograded by ~1 km due to channel avulsion most likely induced by fishpond construction at the river mouth. Between 1988-1998, erosion occurred likely due to the compounding effects of loss of mangrove cover and an increase in the number of typhoon events during the period. The majority of the coastline became relatively stable from 2004-2006. Progradation occurred at the mouth of Jaro River from 2006-2010. By the end of 2018, shoreline prograded by as much as 1.4 km. Rapid progradation is attributed to both large sediment input and the low accommodation space in the area of new discharge. The estimated volume of sediment deposited annually from 2010 is 4.11 x 105 m3, while the annual sediment input during the progradation phase between 1947-1988 is estimated at 2.70 x 104 m3. The shortened floodwater route likely contributed to the one order magnitude increase in sediment input but an increase of sediment yield in the upper stretches of Jaro River likely had a greater contribution.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"30 1","pages":"134-137"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82479236","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

A Multiclass Deep Learning Approach for LULC Classification of Multispectral Satellite Images 一种多光谱卫星图像LULC分类的多类深度学习方法

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358947

Dinesh Sathyanarayanan, D. Anudeep, C. A. Keshav Das, Sanat Bhanadarkar, U. D, R. Hebbar, K. Raj

{"title":"A Multiclass Deep Learning Approach for LULC Classification of Multispectral Satellite Images","authors":"Dinesh Sathyanarayanan, D. Anudeep, C. A. Keshav Das, Sanat Bhanadarkar, U. D, R. Hebbar, K. Raj","doi":"10.1109/InGARSS48198.2020.9358947","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358947","url":null,"abstract":"In general, a visual interpretation technique is adopted for mapping of Land Use / Land Cover (LULC) using temporal satellite data. Although highly accurate, the process is tedious, time consuming and requires a significant amount of domain knowledge. This limitation introduces a scope for partial automation to reduce manual effort involved in interpretation, while maintaining baseline accuracy. The research explores a novel multi-class training approach using a Deep Learning (DL) model to generate major LULC classes. Five spectral bands, namely Blue, Green, Red, Near-Infrared (NIR) and Short wave Infrared (SWIR) from the Sentinel-2A satellite, covering Mandya, Karnataka, India was used to train the model. An existing LULC map of the region was used as an input for automatically generating labeled training samples and a modified SegNet was implemented for classification. Four major LULC classes of interest - water bodies, forest lands, croplands, built-up were classified with an average F1 score of 0.84. The trained model applied to other regions has shown encouraging results which makes this an effective method to explore the generation of LULC maps.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"77 1","pages":"102-105"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91242796","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: 4

Change Detection of Incident Light Over Indian Sub-Continent During Covid-19 Lockdown Using Satellite Imaging Data 利用卫星成像数据检测2019冠状病毒病封锁期间印度次大陆入射光的变化

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358953

Swastik Bhattacharya, D. Desai

引用次数: 2

Estimation of Aerosol Radiative Forcing Over an Urban Environment Using Radiative Transfer Model 利用辐射传输模式估算城市环境气溶胶辐射强迫

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358932

Yash Dahima, Tejas Turakhia, A. Chhabra, Rajesh C. Iyer

{"title":"Estimation of Aerosol Radiative Forcing Over an Urban Environment Using Radiative Transfer Model","authors":"Yash Dahima, Tejas Turakhia, A. Chhabra, Rajesh C. Iyer","doi":"10.1109/InGARSS48198.2020.9358932","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358932","url":null,"abstract":"This study has been carried out to understand the effect of aerosols on the urban climate of Gandhinagar. As a part of it, we have carried out measurements of Aerosol Optical Depth at selected locations of Gandhinagar for winter, summer and post-monsoon seasons of 2017 and 2018, respectively. An analysis of short-wave Aerosol Direct Radiative Forcing (ADRF) is done using these measurements as inputs to the SBDART model. The hourly averaged ADRF of the atmosphere 73.6 Wm−2 in 2017 and 69.6 Wm−2 in 2018, indicates that a high amount of energy was trapped within the atmosphere by aerosols which results in the heating of the atmosphere. This model estimation indicates that atmospheric radiative forcing occurs in all the seasons, but much more strongly during the summer season. A large difference between SURF and TOA forcing in the summer season indicates large absorption of the radiant energy (~95.5 Wm−2) within the atmosphere. Correlation between AOD and aerosol radiative forcing has also been calculated as an attempt is made to estimate the dependency of ADRF on a very import optical property of aerosols.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"22 1","pages":"185-188"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91531705","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

Vehicle Tracking Using Morphological Properties for Traffic Modelling 基于形态属性的交通建模车辆跟踪

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358966

Varsha Kshirsagar-Deshpande, T. Patel, Ali Abbas, Khushbhu Bhatt, R. Bhalerao, Jiten Shah

引用次数: 1

Hyperspectral image classification using semi-supervised learning with label propagation 基于标签传播的半监督学习的高光谱图像分类

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358921

Usha Patel, Hardik Dave, Vibha Patel

引用次数: 3

Index Based Extraction of Impervious Surfaces Using RGB and NIR Band Combinations in AVIRIS-NG Hyperspectral Imagery 基于索引的AVIRIS-NG高光谱影像中RGB和NIR波段组合不透水面提取

2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS) Pub Date : 2020-12-01 DOI: 10.1109/InGARSS48198.2020.9358942

Dwijendra Pandey, Kailash Chandra Tiwari

引用次数: 0