The lack of common semantic information among corresponding geo-objects in different datasets required new matching approaches based on geometric and topological measures. In this study, a semi-automated matching approach based on the matching capabilities of geometric and topological measures was proposed. In the first stage, after the initial matching performed by a scoring system, the efficiency of each measure on the matching accuracy is evaluated manually by an operator. In the second stage, (1) the score of each measure is updated in accordance with the accuracy distributions. This means that the score of a measure is increased if it is relatively more significant than others. Finally, (2) matching process is repeated with new scores. The proposed approach was tested by matching tree-, cellular-, and hybrid-patterned road lines in municipal, private navigation, and OpenStreetMap datasets. The experimental testing shows that it has satisfactory results both in accuracy and completeness. F-measure is over 86% in hybrid-patterned Bosphorus datasets.
{"title":"A new approach for matching road lines using efficiency rates of similarity measures","authors":"M. Hacar, T. Gökgöz","doi":"10.26833/IJEG.791324","DOIUrl":"https://doi.org/10.26833/IJEG.791324","url":null,"abstract":"The lack of common semantic information among corresponding geo-objects in different datasets required new matching approaches based on geometric and topological measures. In this study, a semi-automated matching approach based on the matching capabilities of geometric and topological measures was proposed. In the first stage, after the initial matching performed by a scoring system, the efficiency of each measure on the matching accuracy is evaluated manually by an operator. In the second stage, (1) the score of each measure is updated in accordance with the accuracy distributions. This means that the score of a measure is increased if it is relatively more significant than others. Finally, (2) matching process is repeated with new scores. The proposed approach was tested by matching tree-, cellular-, and hybrid-patterned road lines in municipal, private navigation, and OpenStreetMap datasets. The experimental testing shows that it has satisfactory results both in accuracy and completeness. F-measure is over 86% in hybrid-patterned Bosphorus datasets.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43087509","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}
Inverse synthetic aperture radar (ISAR) imaging is a reliable detection and classification technique for maneuvering targets at near and far-field ranges. In this study, we examine the near-field circular (turntable) ISAR imaging by conducting various real measurement experiments that were performed in the microwave anechoic chamber of the Mersin University’s MEATRC laboratory. The backscattered data were collected via a vector network analyzer that works as a Stepped Frequency Continuous Wave (SFCW) radar and for a number of simple and complex metal objects. The collected raw data were calibrated by using the backscattering data of a canonical object and then focused by applying a near-field backprojection image reconstruction algorithm. The resultant circular ISAR images demonstrate successful and well localized detection of various types of targets even though they are camouflaged by clothing. The obtained results reveal the preliminary efficacy of C band ISAR imaging in concealed object detection problem encountered at security checkpoints such as airports.
{"title":"ANECHOIC CHAMBER MEASUREMENTS FOR CIRCULAR ISAR IMAGING AT MERSIN UNIVERSITY’S MEATRC LAB","authors":"S. Demirci, C. Ozdemir","doi":"10.26833/ijeg.649961","DOIUrl":"https://doi.org/10.26833/ijeg.649961","url":null,"abstract":"Inverse synthetic aperture radar (ISAR) imaging is a reliable detection and classification technique for maneuvering targets at near and far-field ranges. In this study, we examine the near-field circular (turntable) ISAR imaging by conducting various real measurement experiments that were performed in the microwave anechoic chamber of the Mersin University’s MEATRC laboratory. The backscattered data were collected via a vector network analyzer that works as a Stepped Frequency Continuous Wave (SFCW) radar and for a number of simple and complex metal objects. The collected raw data were calibrated by using the backscattering data of a canonical object and then focused by applying a near-field backprojection image reconstruction algorithm. The resultant circular ISAR images demonstrate successful and well localized detection of various types of targets even though they are camouflaged by clothing. The obtained results reveal the preliminary efficacy of C band ISAR imaging in concealed object detection problem encountered at security checkpoints such as airports.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42352708","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}
G. Arasan, A. Yilmaz, Orhan Firat, Ertuğrul Avşar, H. Güner, Kemal Ayğan, Damla Yüce
Optical satellite imagery has an important place today in terms of responding to the increasing need for geospatial base in many different fields and disciplines, especially because of their availability and temporal resolution. Because all kinds of geospatial information and data production processes such as orthoimages, maps, vector data and etc. in especially for large project areas provide the opportunity to reduce the cost and time required in the field work, so the interest in high resolution satellite imagery. Gokturk-1, an electro-optical satellite that was launched on December 5, 2016 and acquiring 0.50 m spatial resolution imagery, aims to meet the high resolution image requirements of Turkey. In this study, the horizontal and vertical accuracy of the Digital Surface Model and orthoimages produced by different methods from stereo images obtained from Gokturk-1 satellite in two different regions were investigated. As a result, although the pointing accuracy and the Digital Surface Model accuracy produced from Gokturk-1 satellite imagery, will vary according to the incidence angle of Gokturk-1 satellite, the Digital Terrain Model used in the production of the orthoimage, the selected method for orientation of satellite imagery; a planimetric accuracy of better than ± 2 m RMSE in orthoimage and a height accuracy of better than ± 3 m RMSE is accomplished.
光学卫星图像今天在响应许多不同领域和学科对地理空间基础日益增长的需求方面具有重要地位，特别是因为它们的可用性和时间分辨率。由于各种地理空间信息和数据的生产过程如正射影图、地图、矢量数据等在特别为大型项目区提供了减少实地工作所需成本和时间的机会，因此人们对高分辨率卫星图像产生了兴趣。Gokturk-1是一颗光电卫星，于2016年12月5日发射，获取0.50 m空间分辨率图像，旨在满足土耳其的高分辨率图像需求。本文研究了Gokturk-1卫星在两个不同区域的立体影像，采用不同方法生成的数字地表模型和正射影的水平和垂直精度。因此，尽管Gokturk-1卫星图像产生的指向精度和数字表面模型精度会根据Gokturk-1卫星的入射角而变化，但用于生产正射影像的数字地形模型是卫星图像定向的选择方法;正射影像的平面精度优于±2 m RMSE，高度精度优于±3 m RMSE。
{"title":"ACCURACY ASSESSMENT OF GÖKTÜRK-1 SATELLITE IMAGERY","authors":"G. Arasan, A. Yilmaz, Orhan Firat, Ertuğrul Avşar, H. Güner, Kemal Ayğan, Damla Yüce","doi":"10.26833/ijeg.650899","DOIUrl":"https://doi.org/10.26833/ijeg.650899","url":null,"abstract":"Optical satellite imagery has an important place today in terms of responding to the increasing need for geospatial base in many different fields and disciplines, especially because of their availability and temporal resolution. Because all kinds of geospatial information and data production processes such as orthoimages, maps, vector data and etc. in especially for large project areas provide the opportunity to reduce the cost and time required in the field work, so the interest in high resolution satellite imagery. Gokturk-1, an electro-optical satellite that was launched on December 5, 2016 and acquiring 0.50 m spatial resolution imagery, aims to meet the high resolution image requirements of Turkey. In this study, the horizontal and vertical accuracy of the Digital Surface Model and orthoimages produced by different methods from stereo images obtained from Gokturk-1 satellite in two different regions were investigated. As a result, although the pointing accuracy and the Digital Surface Model accuracy produced from Gokturk-1 satellite imagery, will vary according to the incidence angle of Gokturk-1 satellite, the Digital Terrain Model used in the production of the orthoimage, the selected method for orientation of satellite imagery; a planimetric accuracy of better than ± 2 m RMSE in orthoimage and a height accuracy of better than ± 3 m RMSE is accomplished.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44575525","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}
Object detection and classification are among the most popular topics in Photogrammetry and Remote Sensing studies. With technological developments, a large number of high-resolution satellite images have been obtained and it has become possible to distinguish many different objects. Despite all these developments, the need for human intervention in object detection and classification is seen as one of the major problems. Machine learning has been used as a priority option to this day to reduce this need. Although success has been achieved with this method, human intervention is still needed. Deep learning provides a great convenience by eliminating this problem. Deep learning methods carry out the learning process on raw data unlike traditional machine learning methods. Although deep learning has a long history, the main reasons for its increased popularity in recent years are; the availability of sufficient data for the training process and the availability of hardware to process the data. In this study, a performance comparison was made between two different convolutional neural network architectures (SegNet and Fully Convolutional Networks (FCN)) which are used for object segmentation and classification on images. These two different models were trained using the same training dataset and their performances have been evaluated using the same test dataset. The results show that, for building segmentation, there is not much significant difference between these two architectures in terms of accuracy, but FCN architecture is more successful than SegNet by 1%. However, this situation may vary according to the dataset used during the training of the system.
{"title":"FEATURE EXTRACTION FROM SATELLITE IMAGES USING SEGNET AND FULLY CONVOLUTIONAL NETWORKS (FCN)","authors":"Batuhan Sariturk, B. Bayram, Z. Duran, D. Seker","doi":"10.26833/ijeg.645426","DOIUrl":"https://doi.org/10.26833/ijeg.645426","url":null,"abstract":"Object detection and classification are among the most popular topics in Photogrammetry and Remote Sensing studies. With technological developments, a large number of high-resolution satellite images have been obtained and it has become possible to distinguish many different objects. Despite all these developments, the need for human intervention in object detection and classification is seen as one of the major problems. Machine learning has been used as a priority option to this day to reduce this need. Although success has been achieved with this method, human intervention is still needed. Deep learning provides a great convenience by eliminating this problem. Deep learning methods carry out the learning process on raw data unlike traditional machine learning methods. Although deep learning has a long history, the main reasons for its increased popularity in recent years are; the availability of sufficient data for the training process and the availability of hardware to process the data. In this study, a performance comparison was made between two different convolutional neural network architectures (SegNet and Fully Convolutional Networks (FCN)) which are used for object segmentation and classification on images. These two different models were trained using the same training dataset and their performances have been evaluated using the same test dataset. The results show that, for building segmentation, there is not much significant difference between these two architectures in terms of accuracy, but FCN architecture is more successful than SegNet by 1%. However, this situation may vary according to the dataset used during the training of the system.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45507952","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}
Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.
{"title":"A NEW COLOR DISTANCE MEASURE FORMULATED FROM THE COOPERATION OF THE EUCLIDEAN AND THE VECTOR ANGULAR DIFFERENCES FOR LIDAR POINT CLOUD SEGMENTATION","authors":"Ali Saglam, N. Baykan","doi":"10.26833/ijeg.709212","DOIUrl":"https://doi.org/10.26833/ijeg.709212","url":null,"abstract":"Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43670900","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}
Virtual reality is an artificial computer-generated environment generally referred as virtual reality environment which can be navigated and interacted with by a user. Street View, which was released by Google in 2007, is an ideal tool to discover places and locations. This service doesn’t only provide spatial information, but also a virtual reality environment for the user. Since this service is only available in certain locations, Google enables users to create a street view with custom panoramic images with the help of Google Maps Application Programming Interface (API) for JavaScript. In this study, it is aimed to integrate body motions with a custom created street view service for Yildiz Technical University Davutpasa Campus which has a historical environment and huge places to discover. Microsoft Kinect for Xbox 360 motion sensor along with Flexible Action and Articulated Skeleton Toolkit (FAAST) interface has been employed for this purpose. This integration provides a low-cost alternative for virtual reality experience. The proposed system can be implemented for virtual museums, heritage sites or planetariums consisting of panoramic images.
虚拟现实是一种人工计算机生成的环境，通常被称为虚拟现实环境，用户可以对其进行导航和交互。谷歌于2007年发布的街景是发现地点的理想工具。该服务不仅为用户提供空间信息，还为用户提供虚拟现实环境。由于这项服务仅在某些地方提供，谷歌允许用户在JavaScript的谷歌地图应用程序编程接口（API）的帮助下创建带有自定义全景图像的街景。在这项研究中，它旨在将身体运动与Yildiz Technological University Davutpasa校区定制的街景服务相结合，该校区拥有历史环境和巨大的探索场所。Microsoft Kinect for Xbox 360运动传感器以及灵活动作和关节骨架工具包（FAAST）接口已被用于此目的。这种集成为虚拟现实体验提供了一种低成本的替代方案。所提出的系统可以用于由全景图像组成的虚拟博物馆、遗产地或天文馆。
{"title":"INTEGRATION OF CUSTOM STREET VIEW AND LOW COST MOTION SENSORS","authors":"Tolga Bakirman, M. U. Gumusay","doi":"10.26833/ijeg.589489","DOIUrl":"https://doi.org/10.26833/ijeg.589489","url":null,"abstract":"Virtual reality is an artificial computer-generated environment generally referred as virtual reality environment which can be navigated and interacted with by a user. Street View, which was released by Google in 2007, is an ideal tool to discover places and locations. This service doesn’t only provide spatial information, but also a virtual reality environment for the user. Since this service is only available in certain locations, Google enables users to create a street view with custom panoramic images with the help of Google Maps Application Programming Interface (API) for JavaScript. In this study, it is aimed to integrate body motions with a custom created street view service for Yildiz Technical University Davutpasa Campus which has a historical environment and huge places to discover. Microsoft Kinect for Xbox 360 motion sensor along with Flexible Action and Articulated Skeleton Toolkit (FAAST) interface has been employed for this purpose. This integration provides a low-cost alternative for virtual reality experience. The proposed system can be implemented for virtual museums, heritage sites or planetariums consisting of panoramic images.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47992098","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}
Decision and policymakers need urban footprint data for monitoring human impact on the urban ecosystem for politics and services. Deriving urban footprint is a challenging work since it has rapidly changing borders. The existing methods for deriving urban footprint map based on raster images have several steps such as determination of indicators and parameters of image classification. These steps limit the process by an operator since they require human decisions. This paper proposes a new rule-based approach for obtaining urban footprint based on Delaunay triangulation among selected centroids of roads and dead-end streets. The selection criterion is determined as maximum road length by using standard deviation operator. To produce urban footprints, this method needs no other data or information apart from road network geometry. This means that the proposed method uses only intrinsic indicators and measures. The experimental study was conducted with OpenStreetMap road data of Washington DC, Madrid, Stockholm, and Wellington. The comparisons with authority data prove that the proposed method is sufficient in many parts of urban and suburban lands.
{"title":"A RULE-BASED APPROACH FOR GENERATING URBAN FOOTPRINT MAPS: FROM ROAD NETWORK TO URBAN FOOTPRINT","authors":"M. Hacar","doi":"10.26833/ijeg.623592","DOIUrl":"https://doi.org/10.26833/ijeg.623592","url":null,"abstract":"Decision and policymakers need urban footprint data for monitoring human impact on the urban ecosystem for politics and services. Deriving urban footprint is a challenging work since it has rapidly changing borders. The existing methods for deriving urban footprint map based on raster images have several steps such as determination of indicators and parameters of image classification. These steps limit the process by an operator since they require human decisions. This paper proposes a new rule-based approach for obtaining urban footprint based on Delaunay triangulation among selected centroids of roads and dead-end streets. The selection criterion is determined as maximum road length by using standard deviation operator. To produce urban footprints, this method needs no other data or information apart from road network geometry. This means that the proposed method uses only intrinsic indicators and measures. The experimental study was conducted with OpenStreetMap road data of Washington DC, Madrid, Stockholm, and Wellington. The comparisons with authority data prove that the proposed method is sufficient in many parts of urban and suburban lands.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46867720","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}
Alidou Sawadogo, Hessels Tim, K. Gündogdu, A. Demir, M. Ünlü, S. Zwart
Accurate estimation of evapotranspiration (ET) is an important factor in water management, especially in irrigated agriculture. Accurate irrigation scheduling requires accurate estimation of ET. The objective of this study was to estimate the actual evapotranspiration (ET a ) by the pySEBAL model and to compare it with the actual evapotranspiration measured by the lysimeter method of soybean crop in Adana, Turkey. Five Landsat 5 Thematic Mapper (TM) images and weather data were used for this study to estimate actual evapotranspiration by the pySEBAL model. The results showed a good relationship between ET a estimated by the pySEBAL model and ET a measured by the lysimeter method, with an R 2 of 0.73, an RMSE of 0.51 mm.day −1 , an MBE of 0.04 mm.day −1 and a Willmott’s index of agreement (d) of 0.90. Based on this study, there is a good relationship between the actual evapotranspiration estimated by the pySEBAL model and the actual evapotranspiration measured by the lysimeter method. Consequently, ET a of soybean crop can be estimated with high accuracy by the pySEBAL model in Adana, Turkey.
{"title":"COMPARATIVE ANALYSIS OF THE PYSEBAL MODEL AND LYSIMETER FOR ESTIMATING ACTUAL EVAPOTRANSPIRATION OF SOYBEAN CROP IN ADANA, TURKEY","authors":"Alidou Sawadogo, Hessels Tim, K. Gündogdu, A. Demir, M. Ünlü, S. Zwart","doi":"10.26833/ijeg.573503","DOIUrl":"https://doi.org/10.26833/ijeg.573503","url":null,"abstract":"Accurate estimation of evapotranspiration (ET) is an important factor in water management, especially in irrigated agriculture. Accurate irrigation scheduling requires accurate estimation of ET. The objective of this study was to estimate the actual evapotranspiration (ET a ) by the pySEBAL model and to compare it with the actual evapotranspiration measured by the lysimeter method of soybean crop in Adana, Turkey. Five Landsat 5 Thematic Mapper (TM) images and weather data were used for this study to estimate actual evapotranspiration by the pySEBAL model. The results showed a good relationship between ET a estimated by the pySEBAL model and ET a measured by the lysimeter method, with an R 2 of 0.73, an RMSE of 0.51 mm.day −1 , an MBE of 0.04 mm.day −1 and a Willmott’s index of agreement (d) of 0.90. Based on this study, there is a good relationship between the actual evapotranspiration estimated by the pySEBAL model and the actual evapotranspiration measured by the lysimeter method. Consequently, ET a of soybean crop can be estimated with high accuracy by the pySEBAL model in Adana, Turkey.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43179459","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}
The earthquake precursory phenomena detection using ionospheric perturbation characteristics is a new technique used by the scientist now days. This paper focuses a new technique for detecting any modification in the time series profile shape caused by an impending earthquake to identify precursors as well as an image processing technique for epicenter detection. For this purpose IGS Global Navigation Satellite System (GNSS) Total Electron Content Data (TEC) are utilized from different stations across the world. From the experiment it is observed that the method may detect earthquake precursors a few hours or days prior to the main event due to ionospheric perturbations induced by initiation of earthquake process.
{"title":"A NOVEL APPROACH FOR IONOSPHERIC TOTAL ELECTRON CONTENT EARTHQUAKE PRECURSOR AND EPICENTER DETECTION FOR LOW-LATITUDE","authors":"S. Kalita, B. Chetia","doi":"10.26833/ijeg.614856","DOIUrl":"https://doi.org/10.26833/ijeg.614856","url":null,"abstract":"The earthquake precursory phenomena detection using ionospheric perturbation characteristics is a new technique used by the scientist now days. This paper focuses a new technique for detecting any modification in the time series profile shape caused by an impending earthquake to identify precursors as well as an image processing technique for epicenter detection. For this purpose IGS Global Navigation Satellite System (GNSS) Total Electron Content Data (TEC) are utilized from different stations across the world. From the experiment it is observed that the method may detect earthquake precursors a few hours or days prior to the main event due to ionospheric perturbations induced by initiation of earthquake process.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43324101","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}
Ambiguity resolution plays an important role in surveying using Precise Point Positioning (PPP) and relative positioning techniques that require high accuracy. In this study, ambiguity resolution performance of PPP and relative positioning under the unobstructed (with 7° cut-off angle) and constrained environment (with 25° cut-off angle, such as nearby buildings and street-canyons) using final/ultra-rapid orbit and clock products are investigated for different observation time. Seventeen globally distributed stations and six baselines of lengths from 270 km to 2100 km are chosen for conducting PPP and relative positioning, respectively. A 31-day period in January 2018 is chosen for processing using 24-, 12-, 6-, 4-, 2- and 1-h observations. The results indicate that sub-mm to cm levels of improvement in horizontal and vertical coordinate components are generally observed with ambiguity resolution for PPP and relative positioning techniques compared to the float counterparts. Moreover, accuracy degradation of ambiguity resolution compared to float solution is observed generally in the vertical component using the 25° elevation cut-off angle for both techniques. As the observation time increases, the accuracy improvements from ambiguity resolution decrease for each technique. In addition, fixing to the wrong integer ambiguities are generally seen with a short observation time and a 25° elevation cut-off angle for both techniques due to the poor satellite geometry. As far as baseline length in relative technique is concerned, the testing results show that there is no direct relation between baseline length and the accuracy improvement from ambiguity resolution compared to the float solution. The results also reveal that the coordinates obtained from ambiguity resolution does not significantly change in the relative technique using final or ultra-rapid orbit/clock products, whereas the changes in PPP are significant for most of the stations.
{"title":"PERFORMANCE ANALYSIS OF AMBIGUITY RESOLUTION ON PPP AND RELATIVE POSITIONING TECHNIQUES: CONSIDERATION OF SATELLITE GEOMETRY","authors":"Sermet Ogutcu","doi":"10.26833/ijeg.580027","DOIUrl":"https://doi.org/10.26833/ijeg.580027","url":null,"abstract":"Ambiguity resolution plays an important role in surveying using Precise Point Positioning (PPP) and relative positioning techniques that require high accuracy. In this study, ambiguity resolution performance of PPP and relative positioning under the unobstructed (with 7° cut-off angle) and constrained environment (with 25° cut-off angle, such as nearby buildings and street-canyons) using final/ultra-rapid orbit and clock products are investigated for different observation time. Seventeen globally distributed stations and six baselines of lengths from 270 km to 2100 km are chosen for conducting PPP and relative positioning, respectively. A 31-day period in January 2018 is chosen for processing using 24-, 12-, 6-, 4-, 2- and 1-h observations. The results indicate that sub-mm to cm levels of improvement in horizontal and vertical coordinate components are generally observed with ambiguity resolution for PPP and relative positioning techniques compared to the float counterparts. Moreover, accuracy degradation of ambiguity resolution compared to float solution is observed generally in the vertical component using the 25° elevation cut-off angle for both techniques. As the observation time increases, the accuracy improvements from ambiguity resolution decrease for each technique. In addition, fixing to the wrong integer ambiguities are generally seen with a short observation time and a 25° elevation cut-off angle for both techniques due to the poor satellite geometry. As far as baseline length in relative technique is concerned, the testing results show that there is no direct relation between baseline length and the accuracy improvement from ambiguity resolution compared to the float solution. The results also reveal that the coordinates obtained from ambiguity resolution does not significantly change in the relative technique using final or ultra-rapid orbit/clock products, whereas the changes in PPP are significant for most of the stations.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41477041","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}
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