2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)最新文献_第10页

GIS application in environmental monitoring and risk assessment GIS在环境监测与风险评估中的应用

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849269

Li Chen, Yiying Mao, Ruotong Zhao

{"title":"GIS application in environmental monitoring and risk assessment","authors":"Li Chen, Yiying Mao, Ruotong Zhao","doi":"10.1109/ICGMRS55602.2022.9849269","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849269","url":null,"abstract":"GIS techniques are becoming the mainstream tool in different disciplines such as assessments of biomass resources, mineral resource analysis, groundwater, and air quality investigation. This study explored its application in solving environmental problems monitoring and risk assessment. GIS application in environmental monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in water supply system monitoring and soil heavy metal concentration monitoring, respectively. In addition, it can achieve real-time geographic location information transmission accurately and monitor in various fields by combining the Alibaba Cloud elastic computing service server, user management development environment, real-time data display, wireless sensor network, Arduino microcontroller, and a series of sensors. Combined with the Radial Basis Function Network model and spatial data, GIS technology could monitor and assess the degree of soil wind erosion hazard by quantifying the various indicators of soil wind erosion. GIS can also be applied to assess the environmental risks from water, land, and atmosphere. Based on geological and geomorphological data, the integration of remote sensing and GIS can complete the assessment of flash flood disasters, groundwater exploration, and groundwater pollution. By using the spatial analysis and data processing capabilities of GIS and combined with other technologies or methods such as digital elevation model and Pollution Index, topographic changes, soil properties, and heavy metal pollution can be assessed. GIS provides the ability to query spatial data and translates existing spatial patterns into measurable targets with its built-in analytical tools. It can be used to predict and assess air quality prediction. Through these approaches, relevant authorities can manage different areas rationally and targeted manner, which has practical and long-term implications.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126400685","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

Thick Cloud Removal and Reconstruction for Remote Sensing Images Using Attention-based Deep Neural Networks 基于注意力的深度神经网络遥感图像厚云去除与重建

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849317

Yidan Wang, Q. Xin, Kun Xiao

{"title":"Thick Cloud Removal and Reconstruction for Remote Sensing Images Using Attention-based Deep Neural Networks","authors":"Yidan Wang, Q. Xin, Kun Xiao","doi":"10.1109/ICGMRS55602.2022.9849317","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849317","url":null,"abstract":"Thick cloud removal for remote sensing images is an important and challenging task for researchers. Existed clouds removal methods always have some limitations with a large area of clouds or a long period between the cloudy image and the supplementary cloud-free image. In this paper, we proposed a deep-learning based framework for thick clouds removal. The method added prior spectral information into the model inputs and used deep convolutional neural networks (CNN) with dense connection and channel attention to reconstruct the cloudy areas. The loss function considered both spectral and structure similarity. We designed artificial and observed data experiments to show the performance of the network. Our method achieved the coefficient of determination (R2) of 0.976, structural similarity (SSIM) of 0.937 and root mean squared error (RMSE) of 0.016 in the artificial dataset and can generate reconstruction results with consistent spectral information and clear texture details, indicating that the proposed method is effective for cloud removal and data reconstruction.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131423323","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

Experimental study on remote sensing observation of extinction coefficient of smoke aerosols 烟雾气溶胶消光系数的遥感观测实验研究

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849256

Shiting Sheng, Jun Pan, Lijun Jiang, Yehan Sun

{"title":"Experimental study on remote sensing observation of extinction coefficient of smoke aerosols","authors":"Shiting Sheng, Jun Pan, Lijun Jiang, Yehan Sun","doi":"10.1109/ICGMRS55602.2022.9849256","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849256","url":null,"abstract":"Forest fires and other combustions produce many particulate matter smoke aerosols. The study of optical properties is significant for smoke recognition, remote sensing image correction, etc. To obtain the extinction coefficient of biomass combustion, this study adopts the remote sensing simulation experimental observation method, based on scattering-absorption theory and Bouger-Lambert’s law, establishes the correlation between the extinction coefficient and transmittance of smoke aerosols, and conducts experimental observations under different background objects and different field angles of view, realizes the inversion of the extinction coefficient based on the least-squares regression analysis method, and determines the value and change law of the extinction coefficient of smoke aerosol. The results show that the relationship between the transmittance and concentration of smoke aerosols in the visible light-near-infrared band is in line with the Bouger-Lambert law. The extinction coefficients of different observation field angles and material backgrounds have consistent value rules. The extinction coefficient of the visible light band is more significant in general, and the peak occurs near 700nm.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131226504","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

Building Instance Change Detection from High Spatial Resolution Remote Sensing Images with Improved Instance Segmentation Architecture 基于改进实例分割体系的高空间分辨率遥感图像建筑实例变化检测

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1007/s12524-022-01601-z

Li Yan, Jianbing Yang, Yi Zhang

引用次数: 1

Construction of basic geospatial information framework in Huludao City 葫芦岛市基础地理空间信息框架建设

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849378

Songyan Wang, Ye Wen, Zhe Wang

引用次数: 0

Building Segmentation of UAV-based Oblique Photography Point Cloud Using DoPP and DBSCAN 基于DoPP和DBSCAN的无人机斜摄点云建筑分割

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849376

Guodong Wang, Qiang Wang, R. Zhao, Chao Chen, Yan-xin Lu

引用次数: 1

Spatial-temporal characteristics of vegetation coverage and its relationship with environmental factors in the Three-river headwaters region 三江源区植被覆盖度时空特征及其与环境因子的关系

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849327

Weiyi Huang, Xiao Chang, Qilin Qu, Jianzhou Bai

{"title":"Spatial-temporal characteristics of vegetation coverage and its relationship with environmental factors in the Three-river headwaters region","authors":"Weiyi Huang, Xiao Chang, Qilin Qu, Jianzhou Bai","doi":"10.1109/ICGMRS55602.2022.9849327","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849327","url":null,"abstract":"In order to explore the temporal and spatial characteristics of vegetation coverage and its relationship with environmental factors in the Three-River headwaters region, this study adopted MODIS MYD13Q1 land vegetation data product with a spatial resolution of 250 m and a time interval of 16 days. The vegetation coverage was retrieved based on the improved pixel binary model, and the relationship between the elevation, temperature and precipitation data was analyzed. The results show: 1) From the spatial feature, the vegetation coverage gradually increases from northwest to southeast in the three-river headwaters region, and the boundary is more obvious. The overall vegetation coverage distribution in the three-river source region is as follows: the source region of the Yellow River > the source region of the Yangtze River > the source region of the Lancang River. 2) From the temporal characteristics, In 2019, the monthly normalized vegetation index showed obvious seasonal changes. From January to July, the normalized vegetation index increased from 0.06 to 0.29, and then decreased to 0.09 from August to December. From 2000 to 2019, the average annual vegetation coverage showed a slow upward trend on the whole, with the vegetation coverage between 0.40 and 0.45. In 2015, there was a process of first declining and then rising. 3) Environmental factors: On the whole, the vegetation coverage decreased with the increase of elevation, and the value of normalized vegetation index reached the maximum at 3900-4000 m. Locally, there is a fluctuation at 4500 m; The correlation coefficients between accumulated annual precipitation and accumulated annual mean temperature and NORMALIZED vegetation index are 0.26 and 0.72, respectively, indicating that temperature has a great influence on vegetation coverage.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124202289","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

Analysis on Land-Use/Cover Change in Hangzhou Bay, China during 2000–2020 Using the Google Earth Engine 基于Google Earth Engine的2000-2020年杭州湾土地利用/覆被变化分析

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849258

Jintao Liang, Chao Chen, Haozhe Sun, Zili Zhang

{"title":"Analysis on Land-Use/Cover Change in Hangzhou Bay, China during 2000–2020 Using the Google Earth Engine","authors":"Jintao Liang, Chao Chen, Haozhe Sun, Zili Zhang","doi":"10.1109/ICGMRS55602.2022.9849258","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849258","url":null,"abstract":"Large-scale, long-time series and high-precision land use mapping are the basis for urban planning and environmental protection. Based on Google Earth Engine (GEE) and Landsat satellite remote sensing imagery, we used a random forest (RF) classification algorithm to create the 2000-2020 Hangzhou Bay, China land-use/cover change (LUCC) dataset, extracted the area of each feature based on classified pixels, and studied the spatial and temporal characteristics of LUCC, and the change mechanism. The main results are as follows: (1) The GEE platform can achieve efficient extraction of LUCC data with an overall accuracy (OA) mean value of 91.95% and a Kappa coefficient of 88.87%. (2) The area of construction area has been increasing (+2015.18km2) and the area of cultivated land has been decreasing (-1919.38km2) in the past two decades. (3) The area of bare land (+404.60km2), forest land (-10.01km2), and water bodies (-49.20km2) fluctuate and change. (4) The area of mudflats is decreasing on the north coast, and the area of mudflats on the south coast is gradually moving north, with fluctuating changes. The overall mudflat area decreases (-76.86km2). This study provides data support for the scientific management of land resources in the Hangzhou Bay region, and the resulting dataset is important for the sustainable development of the area.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123558142","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

TOPS model image registration study in topographic undulating areas 地形起伏区TOPS模型图像配准研究

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849381

Wenting Liu, L. Han, Jie Yu

引用次数: 0

A Novel Unsupervised Evaluation Metric for SAR Image Segmentation Results 一种新的SAR图像分割结果的无监督评价度量

2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849399

Hang Yu, X. Yin, Zhiheng Liu, Zichuan Xie, Suiping Zhou, Yuru Guo

{"title":"A Novel Unsupervised Evaluation Metric for SAR Image Segmentation Results","authors":"Hang Yu, X. Yin, Zhiheng Liu, Zichuan Xie, Suiping Zhou, Yuru Guo","doi":"10.1109/ICGMRS55602.2022.9849399","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849399","url":null,"abstract":"The segmentation of Synthetic aperture radar (SAR) images is a critical step in remote sensing image analysis. Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation (UE) is essential for comparing segmentation algorithms and the automatic selection of optimal parameters. The ground truth used in the supervised evaluation (SE) metric is highly subjective, and the ground truth of SAR images is hard to obtain. The current UE metrics only depend on a single feature, and it fails for the segmentation results of SAR images containing multiple heterogeneous features. This study proposes a novel UE method to quantitatively measure the quality of SAR image segmentation results to overcome these problems. In this method, gray and texture features are captured firstly, and the two elements of each segment are fused to the covariance matrix of a segment. Secondly, using the covariance matrix calculates the intra-segment homogeneity and inter-segment heterogeneity of the segmentation results. Finally, a single metric combines these metrics, and a global criterion combines these single segment metrics to reveal the segmentation results quality. The method is tested on three segmentation algorithms and ten images. The proposed method is compared with existing UE methods and a SE method to confirm its capabilities. Through comparison, the results verified the effectiveness of the proposed metric and demonstrated the reliability and improvements of proposed method concerning other methods.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488353","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