مهندسی فناوری اطلاعات مکانی最新文献

{"title":"Improving Classification Accuracy of Hyperspectral Image Using Convolutional Neural Networks","authors":"Mahsa Tekyeh-Nejad, Ata Allah Ebrahimzadeh, Maliheh Ahmadi","doi":"10.61186/jgit.11.1.59","DOIUrl":"https://doi.org/10.61186/jgit.11.1.59","url":null,"abstract":"Hyperspectral image classification is a crucial aspect of remote sensing image analysis. Deep learning methods have been successfully used to classify remote sensing data. In recent years, convolutional neural networks (CNNs) have been significantly used in hyperspectral image classification, which has tried to overcome the computational and processing challenges of hyperspectral data. By increasing the number of parameters and layers of convolutional neural networks, their efficiency in solving complex problems decreases. For this reason, in this article, a new architecture of convolutional neural networks has been introduced, this network has a good performance and reduces the computing time.","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195039","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}

{"title":"Filtering Radar Interferometry Time Series with Univariate Least Squares Noise Matrix Analysis","authors":"Mohsen Zaynalpoor, Hamid Mehrabi, Alireza Amiri","doi":"10.61186/jgit.11.1.37","DOIUrl":"https://doi.org/10.61186/jgit.11.1.37","url":null,"abstract":"Human life is always affected by various natural events such as earthquakes, volcanoes, subsidence, etc. One of the suitable tools for investigating and analyzing these hazards is synthetic aperture radar interferometry. This geodetic technique has the capability of resolving the displacement of the Earth's crust and analyzing the deformation through phase differences of radar images. The main advantage of the InSAR is the high temporal and spatial resolution. Analogous to other geodetic methods, the accuracy of the result depends on the modeling of observational disturbances and noises. Despite progress in recent decades, these disorders have received little attention. The case study is northwest of Hawaii Island. In this study, filtering and reducing the turbulence in time series is based on the most appropriate functional model and stochastic model. This process is done using the MLE test. In this study, functional models include trend, cyclic, and offset. Statistical models also include white noise, flicker, and random walk, whose components are identified through univariate least squares noise analysis. Time series are reproduced through the best functional and statistical models. The results indicate that the best model is the linear trend with the presence of cyclic and offset, and white noise for all pixels. By implementing the univariate least squares noise analysis method, the accuracy of the results improved on average by 43%. In addition, applying both high-pass and low-pass filters resulted in an average improvement of 28%.","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195201","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}

{"title":"Path Optimization with Genetic Algoritm (Case Study: Road of Damghan to Dibaj in Semnan County)","authors":"Meysam Saljughi, Mohammad Hajeb, Aliakbar Matkan","doi":"10.61186/jgit.11.1.83","DOIUrl":"https://doi.org/10.61186/jgit.11.1.83","url":null,"abstract":"The existence of a proper road network is one of the factors of economical growth and sustainable development. Traditional routing methods are time-consuming and costly. In addition, the horizontal and vertical components of the route are taken into consideration separately. Since 1970, efforts have been made to automate routing optimization. The Genetic Algorithm is a heuristic method that is used for solving different optimization problems. This research uses the genetic algorithm for path optimization. This algorithm takes both horizontal and vertical dimensions into consideration simultaneously. Chromosomes are defined as a vector array of station points. The suggested method was implemented for the route of Damghan to Dibaj. At first this research explores the importance of the objective functions of the existing route by using an innovative method with an inverse modeling approach. The results show that the share of the length factor is only 10%, so the low degree of the importance of the path length function imposes a lot of cost on the path users, and as a result, it is the main factor of the instability of the existing path. In order to improve the performance, the algorithm parameters were tuned on a simulation region before the final implementation. The objective functions are: route length, technical and engineering, economical, geological and environmental principles. In the final implementation, the algorithm specifies a corridor for the path at the level of the detailed routing. Then in semi-detailed level, the best paths in this corridor will be found. At the end, the optimal alignment is determined at the executive level. Finally the circular arches were implemented automatically based on the Policy and Geometric Design of Highways. By comparing the proposed alignment with the existing road, it shows a reduction in the length of the road by 9.1 km (%18), and 20% less passing than high-cost landuses. The present study shows the high ability of the genetic algorithm in path optimization.","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195202","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}

{"title":"Performance evaluation of three deep learning models in building footprint extraction from aerial and satellite images","authors":"Nima Ahmadian, Amin Sedaghat, Nazila Mohammadi","doi":"10.61186/jgit.11.1.105","DOIUrl":"https://doi.org/10.61186/jgit.11.1.105","url":null,"abstract":"Performance evaluation of three deep learning models in building footprint extraction from aerial and satellite images","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195038","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}

{"title":"Improving the urban features classification accuracy by fusion of optical and radar high spatial resolution images","authors":"Fattane Kia, Mohammad javad valadan Zoej, Fahimeh Yousefi","doi":"10.61186/jgit.11.1.1","DOIUrl":"https://doi.org/10.61186/jgit.11.1.1","url":null,"abstract":"Improving the urban features classification accuracy by fusion of optical and radar high spatial resolution images","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195040","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}

{"title":"Forest Classification Using Simulated Compact Polarimetry Data and Deep Learning Networks","authors":"Sahar Ebrahimi, Hamid Ebadi, Amir Aghabalaei","doi":"10.61186/jgit.11.1.19","DOIUrl":"https://doi.org/10.61186/jgit.11.1.19","url":null,"abstract":"","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195037","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}

{"title":"Personalization of a tourism recommender system based on users similarity and the use of deep belief network","authors":"Zeinab neisani samani, Ali Asghar Alesheikh, Abolghasem Sadeghi-Niaraki, Mahdi Nazari Ashani","doi":"10.61186/jgit.10.4.1","DOIUrl":"https://doi.org/10.61186/jgit.10.4.1","url":null,"abstract":"Personalization of a tourism recommender system based on users similarity and the use of deep belief network","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135532929","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}

{"title":"Improved Geometric Calibration Method for Thermal Sensors Using the Hough Transform Algorithm","authors":"Soroush Motayyeb, Farhad Samadzadegan, Masood Varshosaz","doi":"10.61186/jgit.10.4.39","DOIUrl":"https://doi.org/10.61186/jgit.10.4.39","url":null,"abstract":"Non-metric thermal sensors have been out of calibration in the laboratory for an extended period of time and require calibration to adjust for the interior orientation parameters and lens distortions. To generate photogrammetric products with the desired degree of geometric precision, it is important to identify the geometric calibration parameters of the non-metric sensor in order to minimize the relative orientation error and resolve the bundle adjustment. The purpose of this research is to present a novel method for geometric calibration of non-metric thermal sensors as a necessary preprocessing step before producing photogrammetric products with the desired geometric precision. To geometrically calibrate the non-metric thermal sensor, the proposed method employs a calibration pattern in the form of a rectangular plate composed of hollow circular targets with symmetrical placement geometry. Hollow circles induce temperature differences, improving the contrast and sharpness of the thermal calibration pattern. Due to the thermal sensors' low spatial resolution and low contrast, circular targets appear as an ellipse in the image. For this reason, in this study, the Hough Transform method is utilized to fit and extract the exact two-dimensional coordinates of the focal center of elliptical targets in the image space. The reason for this is that the Hough Transform employs the parameters of the ellipse to fit it and does not require the entire extraction of its circumferential lines. In the method utilized in this study, the Collinearity Equation is used to compute the geometric calibration elements of the thermal sensor. Various experiments were undertaken to evaluate the proposed approach. The results of these tests, which were performed based on the criterion of Mean Reprojection Error per Image, evaluated the accuracy of the geometric calibration as 0.03 pixels. Additionally, when the proposed method for re-projecting the target´s focal point to the calibration pattern is used in conjunction with the estimated calibration parameters, the mean error between the actual image coordinates and the actual ground coordinates of the targets is reduced to 0.28 pixels when compared to the method of the equation of conic sections.","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135532930","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}

{"title":"Transfer Learning Framework for Semantic Segmentation of High-Resolution UAV-based images in Urban Area","authors":"Abbas Majidizadeh, Hadiseh Hasani, Marzieh Jafari","doi":"10.61186/jgit.10.4.87","DOIUrl":"https://doi.org/10.61186/jgit.10.4.87","url":null,"abstract":"Transfer Learning Framework for Semantic Segmentation of High-Resolution UAV-based images in Urban Area","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135532927","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}

{"title":"Providing The Classification And Prediction of PM2.5 Pollutant Map Using Machine Learning Methods And Extracting Association Rules","authors":"Mohammad Reza Heydari, Parham Pahlavani, Behnaz Bigdeli","doi":"10.61186/jgit.10.4.67","DOIUrl":"https://doi.org/10.61186/jgit.10.4.67","url":null,"abstract":"Providing The Classification And Prediction of PM2.5 Pollutant Map Using Machine Learning Methods And Extracting Association Rules","PeriodicalId":486416,"journal":{"name":"مهندسی فناوری اطلاعات مکانی","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135532931","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}