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

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Research and Practice in Aquaculture Safety Monitoring based on Multi-source Information Fusion 基于多源信息融合的水产养殖安全监测研究与实践
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849230
Yangyan Huang, Huan Hu, Fei Yuan, En Cheng, Junwei Dai
{"title":"Research and Practice in Aquaculture Safety Monitoring based on Multi-source Information Fusion","authors":"Yangyan Huang, Huan Hu, Fei Yuan, En Cheng, Junwei Dai","doi":"10.1109/ICGMRS55602.2022.9849230","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849230","url":null,"abstract":"Research on aquaculture is an important part of environmental science. Aiming at the problem of aquaculture safety monitoring, this paper proposes a detection and judgment method for theft based on multi-source information fusion. The iterative Bayesian inference is used to integrate multi-source information such as abnormal human posture recognition, abnormal sound recognition and occurrence time to obtain relevant data on whether the aquaculture farm has been stolen. The experimental results show that, compared with traditional methods such as video detection or audio detection, the algorithm in this paper can determine whether the aquaculture farm is in a safe, suspicious or stolen state more quickly and accurately, thus providing an accurate disposal basis for managers. At the same time, the algorithm has better robustness.","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":"115701427","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
A full coverage path planning method for unmanned surface vessels 无人水面舰艇全覆盖路径规划方法
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849262
Zhihao Han, Yongzhi Hao, Yu Zhang
{"title":"A full coverage path planning method for unmanned surface vessels","authors":"Zhihao Han, Yongzhi Hao, Yu Zhang","doi":"10.1109/ICGMRS55602.2022.9849262","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849262","url":null,"abstract":"Aiming at the problems of low coverage rate and high path repetition rate for unmanned surface vessels to complete full coverage tasks in irregular areas, the use of remote control navigation puts forward higher requirements for real-time and maneuverability. Therefore, a full coverage path planning system for the USV is designed. The system uses the concave points to segment the irregular task area, and then performs the area reciprocation algorithm for each convex sub-polygon. Finally, the sub-areas are connected based on the improved algorithm of dynamic programming, and the full coverage path planning of the USV is realized. Combined with the actual electronic chart to carry out the full coverage experiment, the coverage rate is high and the repetition rate is low, which can be actually used for the USV full coverage navigation tasks.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"37 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":"124889034","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 Position Extraction Method Combining Visual Saliency and Line Segment Intensity 一种结合视觉显著性和线段强度的位置提取方法
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849350
Xiaoyu Chang, Min Wang, Gang Wang, Feng Gao
{"title":"A Position Extraction Method Combining Visual Saliency and Line Segment Intensity","authors":"Xiaoyu Chang, Min Wang, Gang Wang, Feng Gao","doi":"10.1109/ICGMRS55602.2022.9849350","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849350","url":null,"abstract":"To solve the problems of difficult selection of position samples and high manual dependence of position recognition in most existing researches, this paper proposes a position extraction method combining visual saliency and line segment intensity. In order to verify the effectiveness of the algorithm proposed in this study, the two images were tested, and evaluated the accuracy through quantitative indicators. It can be found that the IoU (Intersection of Union) of the two positions are 0.6658 and 0.5319, respectively, which are all greater than 0.5, indicating the effectiveness of the unsupervised extraction method proposed in this research. The recall rate of the position was all greater than 0.83, indicating that the omission rate of the extracted positions by this method was relatively low, all within 17%. In this study, an unsupervised position extraction method is proposed, which can effectively extract target without training samples, and provides a reliable technical means for rapid unsupervised target recognition.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"24 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":"125015421","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
Seafloor Classification based on Sub-bottom Profiler Data using Random Forest 基于随机森林Sub-bottom profile数据的海底分类
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849348
Yu Luo, Xu Zheng, Jian-gen Shi, Jin Huang
{"title":"Seafloor Classification based on Sub-bottom Profiler Data using Random Forest","authors":"Yu Luo, Xu Zheng, Jian-gen Shi, Jin Huang","doi":"10.1109/ICGMRS55602.2022.9849348","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849348","url":null,"abstract":"Research on Seafloor classification and recognition is of great significance. Sub-bottom profiler is a kind of equipment used to prospect seabed shallow profiler. Its echo signal has rich sediment characteristics and can be used for seabed sediment classification. Based on the data characteristics of dual frequency sub-bottom profiler, this paper accurately extracts the echo intensity sequence containing rich characteristics of seabed sediments by using high-frequency data and threshold detection method. Dynamic and static methods are used to collect sediment data of gravel, silt and cement in the lab testing pond, The dynamic data set, static data set are classified by random forest algorithm. The classification accuracy of the optimized classification model is 98.15%, 85.67%. It proved that this method can be effectively used in the classification of seabed sediment.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"69 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":"114925353","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
Research on geospatial data visualization based on GIS and reverse proxy 基于GIS和反向代理的地理空间数据可视化研究
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849266
Jinku Huang, Ping Li, Wenbin Zhu, Qingling Sun
{"title":"Research on geospatial data visualization based on GIS and reverse proxy","authors":"Jinku Huang, Ping Li, Wenbin Zhu, Qingling Sun","doi":"10.1109/ICGMRS55602.2022.9849266","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849266","url":null,"abstract":"Based on the practical work, this paper discusses the current visualization techniques of geospatial data. Based on the traditional method of publishing geospatial data service by GIS server, when the data service has been published, it is not convenient to change the layer organization structure of the current data service. When the layer combination structure of geospatial data service needs to be changed, it can only re-publish the new data service. When a large number of data services are published, the server occupies a large amount of memory resources and deteriorates the server performance. This paper proposes a visualization method of geospatial data based on GIS and reverse proxy. When the data service has been published, it can flexibly conFigure the layer structure of the data service according to the actual work needs. By means of computer reverse proxy technology, the security of geospatial data sharing is improved, and the problem of GIS server performance degradation caused by re-release of data service is avoided.","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":"122299188","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
Multi-beam Data Automatic Filtering Technology 多波束数据自动滤波技术
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849352
Yu Yan, Linfeng Yuan, Longjiang Ran, Hui Yin, X. Xiao
{"title":"Multi-beam Data Automatic Filtering Technology","authors":"Yu Yan, Linfeng Yuan, Longjiang Ran, Hui Yin, X. Xiao","doi":"10.1109/ICGMRS55602.2022.9849352","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849352","url":null,"abstract":"Aiming at the characteristics of complex noise sources in multi-beam bathymetric data, this paper proposes a multi-beam automatic filtering method that combines filtering of optimal reference curved surface and trend surface. By implementing statistical filtering to pre-process the data, the optimal reference curved surface is constructed based on the filtered terrain data, the theoretical optimal depth value of each beam point is calculated. The optimal reference curved surface is filtered by combining the depth tolerance to determine whether the point is a noise point. Then the trend surface is constructed using the filtered non-noise data, and the trend surface is filtered on the original point cloud data. Through the verification of the measured data, this method can effectively remove most of the cluster noises in the multi-beam bathymetric data and is more efficient than manual processing.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"29 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":"122409040","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
Research on Key Technologies of 3D Real Scene Visualization Based on Multi-source Observation Data Fusion 基于多源观测数据融合的三维真实场景可视化关键技术研究
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849224
Qiang Jing, Hongzhi Miao, Xiaowei Yang, Huifang Ming, Hui Yin
{"title":"Research on Key Technologies of 3D Real Scene Visualization Based on Multi-source Observation Data Fusion","authors":"Qiang Jing, Hongzhi Miao, Xiaowei Yang, Huifang Ming, Hui Yin","doi":"10.1109/ICGMRS55602.2022.9849224","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849224","url":null,"abstract":"Based on the big data of multi-source observation of the underwater environment of the Hong Kong-Zhuhai-Macao Bridge,this study constructed an underwater three-dimensional(3D) scene with multi-source data fusion,realized the visual presentation and management of the underwater environment of large structures,and satisfied the requirements of visual operation and safety detection and protection of large structures.In this paper,the key technologies related to multisource spatio-temporal big data fusion and 3D real scene visualization were studied,a multi-storage distributed storage framework for hybrid spatio-temporal big data is constructed,a hierarchical organization model for spatio-temporal big data is proposed,and multi-source observation data fusion of large underwater structures is realized.Furthermore,3D scenes are rendered based on technologies such as WebGL,onshore/undersea terrain simulation,and gridding to realize 3D real-world visualization.The construction of a 3D real scene visualization model based on multi-source spatiotemporal big data fusion can quickly realize the deformation monitoring and spatiotemporal deduction of the Hong Kong-Zhuhai-Macao Bridge.Based on the fused 3D scene,the evolution analysis of underwater terrain and stratum can be conducted,which can effectively support the monitoring and management of the underwater environment of large structures.","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":"121990486","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
Research on hydrogeological conditions based on ArcGIS-taking Yongding River's ecological water supplement as an example 基于arcgis的水文地质条件研究——以永定河生态补水为例
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849353
Qiaohui Wang, Guohua Chen, Yingzhen Huang, Qi Wang, Lili Cao
{"title":"Research on hydrogeological conditions based on ArcGIS-taking Yongding River's ecological water supplement as an example","authors":"Qiaohui Wang, Guohua Chen, Yingzhen Huang, Qi Wang, Lili Cao","doi":"10.1109/ICGMRS55602.2022.9849353","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849353","url":null,"abstract":"With the progress of digitization and the continuous development of GIS, it is convenient for us to better understand, manage and coordinate the geographical information display in engineering application from all levels. This paper introduces the extraction and conversion of geological survey, deep well data and long-term groundwater level monitoring data with ArcGIS mapping technology in the study of hydrogeological conditions in the western part of Beijing, and the standardized drawing of the extracted and transformed data and graphic information. The display and characterization of regional geographic information, analysis of groundwater influencing factors and local fine research are realized, which provides support for research work. By comparing with Sufer and AutoCAD, advantages of ArcGIS software are as follows: 1) It supports multiple data formats and has strong linkage with other software. 2) Rich drawing commands. 3) It has powerful data editing function.","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":"126710871","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
A Neural Network Model-Based Inversion Method for Stratified Seafloor Geoacoustic Parameters in Shallow Water 基于神经网络模型的浅水分层海底地声参数反演方法
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849330
Xiaorou Zhang, Z. Cui, Qile Wang, Hanhao Zhu, Wei Liu, Z. Chai
{"title":"A Neural Network Model-Based Inversion Method for Stratified Seafloor Geoacoustic Parameters in Shallow Water","authors":"Xiaorou Zhang, Z. Cui, Qile Wang, Hanhao Zhu, Wei Liu, Z. Chai","doi":"10.1109/ICGMRS55602.2022.9849330","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849330","url":null,"abstract":"At this stage, in the inversion research of shallow sea geoacoustic parameters, the traditional inversion method focuses on calculating the parameter inversion under the default seafloor layering, and does not clarify the seafloor layering. Therefore, when dealing with the experimental data under the unknown seabed stratification, the geoacoustic parameters that conform to the specific seabed stratification cannot be accurately given. Aiming at such problems, this paper proposes a layered shallow sea geoacoustic parameters inversion method based on a layered physical model of the seafloor and a neural network algorithm model. First, a subsea physical model with a layered structure is selected through simulation, and the fast field sound field method (FFM) is used to calculate the sound field, then the theoretical prediction value of the shallow sea acoustic pressure field under different layered conditions is obtained. Second, the pattern recognition neural network algorithm is established to analyze the characteristics of the predicted sound pressure field under different seabed layers. Finally, according to the layered results, the sound pressure field is substituted into the seabed parameters inversion model of the corresponding layers, and the seabed parameters inversion is performed to obtain the inversion results. Experiments results show that the number of model iterations in the neural network algorithm is less and the efficiency is higher. The problem of seabed stratification is effectively solved through the characteristic analysis of the sound pressure field, which greatly improves the convenience of the inversion work.","PeriodicalId":129909,"journal":{"name":"2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS)","volume":"30 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":"129203726","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
Threat Level Assessment by Imaging Reconnaissance Satellite to Ground Target Based on Fuzzy Evaluation 基于模糊评价的成像侦察卫星对地面目标威胁等级评估
2022 3rd International Conference on Geology, Mapping and Remote Sensing (ICGMRS) Pub Date : 2022-04-22 DOI: 10.1109/ICGMRS55602.2022.9849287
Jian He, Yu Cheng, Hao Li, Shenyi Cao, Xiaotong Deng, Ze Zhang
{"title":"Threat Level Assessment by Imaging Reconnaissance Satellite to Ground Target Based on Fuzzy Evaluation","authors":"Jian He, Yu Cheng, Hao Li, Shenyi Cao, Xiaotong Deng, Ze Zhang","doi":"10.1109/ICGMRS55602.2022.9849287","DOIUrl":"https://doi.org/10.1109/ICGMRS55602.2022.9849287","url":null,"abstract":"In modern warfare, the reconnaissance satellite finds the target and makes the target have the risk of being attacked, in order to estimate the threat degree of imaging reconnaissance satellite to ground target detection during the transit period, this paper proposes a threat level evaluation method of reconnaissance satellite to ground target based on fuzzy evaluation, which comprehensively considers the discovery, recognition, confirmation and discrimination of imaging reconnaissance satellite to target, constructs the threat level fuzzy membership function corresponding to different detection probabilities, uses the fuzzy evaluation criteria and establish the target threat level evaluation model. A set of calculation results of detection probability are given, and the unique threat level is determined. The results show that the algorithm is effective and feasible and it can provide a reference for the effectiveness evaluation of reconnaissance satellite.","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":"130641168","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
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