آنالیز سری زمانی تصاویر راداری پایش نیمه‌خودکار در نظارت بر ساخت‌وساز غیرمجاز شهری (منطقة مورد مطالعه: شهرک‌های مهرآوران، اندیشه و فراز یزد)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکدة فنی و مهندسی، دانشگاه آزاد اسلامی یزد، یزد

2 استاد تمام، گروه سنجش از دور، دانشکدة فنی و مهندسی، دانشگاه آزاد اسلامی یزد، یزد

چکیده

تخلفات ساختمانی، به‌سبب سطح فراگیر و آثار بلندمدت و پایدارشان در نیم‌رخ شهرها، از مهم‌ترین چالش‌های شهرنشینی نوین محسوب می‌شوند. روش‌های رایج و معمول که امروزه در کنترل ساخت‌وسازها استفاده می‌شود، بسیار زمان‌بر و پرهزینه است. هدف اصلی این پژوهش ارائة چارچوبی نوین به‌منظور برآورد سریع و کم‌هزینه، در آشکارسازی و نظارت بر ساخت‌وسازها و شناسایی ساختمان‌های غیرمجاز شهری، با استفاده از تصاویر ماهوارة سنتینل‌ـ 1 در دورة زمانی 2017 تا 2022 و سیستم‌های اطلاعات مکانی است. بدین‌منظور در مرحلة اول، براساس تحلیل و پردازش در نرم‌افزار SNAP، ضریب پراکنش سیگمانات تصاویر استخراج و به دو طبقة ساختمان و غیرساختمان تفکیک شده و حد آستانة بیشتر از 01/0به‌دست آمده است. سپس، با استفاده از الگوریتم پیکسل‌مبنا، تصویر باینری ساختمان و غیرساختمان به‌صورت صفر و یک تهیه و براساس اختلاف دو تصویر، منطقه‌ای که ساخت‌وساز در آن انجام شده است مشخص شد. پس از آشکارسازی مناطق ساختمانی تغییریافته، با استفاده از الگوریتم‌های طبقه‌بندی حداکثر احتمال و جنگل تصادفی، این مناطق در سه کلاس (ساختمان، در حال ساخت و سایر اراضی) قرار گرفتند و با نقشة برداشت میدانی و پارسل‌های بدون پروانه ارزیابی شدند. نتایج نشان داد تعداد ساختمان‌های بدون پروانه با استفاده از الگوریتم حداکثر احتمال، جنگل تصادفی و برداشت میدانی، به‌ترتیب، 130 و 135 و 48 است؛ همچنین دقت اجرای روش حداکثر احتمال به بیشترین میزان 89/0% و ضریب کاپای 83/0% نسبت‌به روش جنگل تصادفی، با دقت کلی 86/0 و ضریب کاپای 81/0% بوده است.

کلیدواژه‌ها

عنوان مقاله [English]

Time Series Analysis of Semi-Automatic Monitoring Radar Images in Monitoring Unauthorized Urban Construction (The Studied Area of Mehravaran, Andisheh and Faraz, Yazd)

نویسندگان [English]

  • Zohreh Salehinezhad 1
  • Seyed ali Almodaresi 2
1 M.Sc. of Remote Sensing and Geographical Information System, Faculty of Engineering, Islamic Azad University of Yazd, Yazd
2 Full Prof., Dep. of Remote Sensing, Faculty of Engineering, Islamic Azad University of Yazd, Yazd
چکیده [English]

Construction violations are considered one of the most important challenges of modern urbanization due to their widespread level and long-term and stable effects on the profile of cities. Construction violations are an important issue for municipalities that can threaten building structures in a city. The traditional methods that are used today to control constructions are very time-consuming and expensive. The main goal of this research is to provide a new framework for quick and low-cost estimation, in revealing and monitoring constructions and identifying unauthorized urban buildings using Sentinel-1 satellite images in the period from 2017 to 2022 and spatial information systems. For this purpose, in the first step, based on the analysis and processing in SNAP software, the Sigma-Notch dispersion coefficient of the images was extracted and separated into two floors of buildings and non-buildings, and a threshold limit of more than 0.01 was obtained. Then, by using pixel based algorithm, the binary image of building and non-building was prepared as zero and one, and based on the difference between the two images, the area where the construction was done was determined. After revealing the changed construction areas, they were classified into three classes (building, under construction, and other lands) using maximum likelihood classification algorithms and random forest, and were evaluated with a field survey map and unlicensed parcels. The results showed that the number of unlicensed buildings using the maximum likelihood algorithm, random forest and field sampling is 97,135 and 48, respectively; Also, the accuracy of the maximum likelihood method was 0.89% and the kappa coefficient was 0.83% compared to the random forest method with the overall accuracy of 0.86 and the kappa coefficient was 0.81%.

کلیدواژه‌ها [English]

  • Radar images
  • Sentinel-1 satellite
  • Semi-automatic monitoring
  • Unauthorized urban buildings
  • Building police

مراجع

Alavi Panah, S.K., 2017, Application of Remote Sensing in Earth Sciences, Tehran: Tehran University Press.
Alishah Arat, B., Arzani, H., Hosseini, S.Z., Babaei Kafaki, S. & Mirakhorlou, Kh., 2011,  Rangelands vegetation cover mapping using IRS-LissIII data image processing (Case study: Sorkh Abad Basin, Mazandaran), Iranian Journal of Range and Desert Reseach, 20(3), PP. 454-462.
Amit, Y. & Geman, D., 1997, Shape Quantization and Recognition with Randomized Trees, Neural Computation, 9, PP. 1545-1588.
Attarchi, S., 2018, The Use of Radar Indices in the Extraction of Urban Impervious Surfaces Using All-Polarimetric Radar Image, City Planning Geography Researches, 7(4), PP. 837-854.
Bayburt, S., Buyuksalih, G. & Baz, I., 2008, Detection of Changes in Istanbul Area with Medium and high-resolution Space Images, Conference: XXIth ISPRS CongressAt: BeijingVolume: ISPRS Archives – Volume XXXVII Part B2: 1607-1611.
Breiman, L., 1996, Out-of-Bag Estimation, Technical report, Statistics Department, University of California Berkeley, Berkeley CA 94708, 1996b. 33, 34.
Einollah Zadeh, S. & Yusuf Pour, J., 2014, Investigation of Factors Affecting Urban Unauthorized Construction and Unauthorized Construction and Its Consequences, International Conference on Architecture, Urban Planning, Civil Engineering, Art and Environment; Future horizons, looking at the past, Tehran.
El-Darymli, K., McGuire, P., Gill, E., Power, D. & Moloney, C., 2014, Understanding the Significance of Radiometric Calibration for Synthetic Aperture Radar Imagery, IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), PP. 1-6.
 
Esmailpour, N., Heravi, Z. & Heydari Hamaneh, E., 2018, Investigating the Causes of Construction Violations in Iranian Cities with an Emphasis on the Role of the Municipality (Case Study: Area 3 of Yazd City), Eighth Year City Studies Quarterly, 31, PP. 17-30.
Esmaili Aliai, E., 2017, Determining and Identifying Factors of Unauthorized Constructions and Providing Optimal Solutions against Them. Case Study: Yazd City (using TOPSIS Model), Taft Azad University.
Fahmideh Madami, M., Ayaz, M., Alajeh Gardi, A. & Javanshiri, M., 2022, Analysis of the Smart Model for Monitoring Construction Violations in Urban Management, a Case Study: The Boundaries and Boundaries of Mashhad Metropolis, Iranian Journal of Remote Sensing & GIS, https://doi.org/10.52547/ gisj.2022.226439.1085.
Golmehr, E. 2017, Investigating the Causes and Effective Factors in Unauthorized Constructions (Case Study of Qazvin City), Geography and Regional Planning Quarterly, 8(3), PP. 101-113.
Hadian, F., Jafari, R., Bushra, H., & Soltani, S., 2011, Investigating Different Groups of Remote Sensing Plant Indices in the Study of Pasture Types (Case Study: Semiram, Isfahan), Pasture Journal, 4, PP. 429-420.
Hermosilla, T., Ruiz, L.A., Recio, J.A. & Estornell, J., 2011, Evaluation of Automatic Building Detection Approaches Combining high-resolution Images and LIDAR Data, International Journal of Remote Sensing, 3, PP. 1188-1210.
Ho, T.K., 1998, The Random Subspace Method for Constructing Decision Forests, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, PP. 832-844.
Horning, N., 2010, Random Forests: An Algorithm for Image Classification and Generation of Continuous Fields Data Sets, In Proceedings of the International
Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences, Osaka, Japan. 911.
İlsever, M. & Unsalan, C., 2012, Two- Dimensional Change Detection Methods: Remote Sensing Applications, Springer Science & Business Media,
Jin, J., 2012, A Random forest-based Method for Urban Land Cover Classification Using Lidar Data and Aerial Imagery, The University of Waterloo.
Kapoor, M. & Blanc, D., 2008, Measuring Risk on Investment in Informal Housing: Theory and Evidence from Pune, India, Regional Science and Urban Economics, 38(4), PP. 311-329.
Khalili Moghadam N, Delavar M R, Hanachi P. Semi-Automatic Illegal Building Detection in Urban Areas Using Satellite Images and a Pixel-Based Fuzzy XOR Operator Method. GEJ 2016; 7 (3) :105-116
Khalili Moghadam, D. & Hanachi, P., 2015, Semi-Automatic Monitoring of Illegal Constructions in Urban Area Using Satellite Images and Pixel Method Based on Implicit Fuzzy Operator, Scientific Journal of Mapping and Geospatial Information Engineering, 7(3), PP. 105-116.
Kovacs, A. & Sziranyi, T., 2012, Orientation-based Building Outline Extraction in Aerial Images, ISPRS Annuals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1-7, PP. 141-146.
Kranjcic, N. & Durin, B., 2021, Unsupervised Classification for Illegal Building Monitoring, Open Access Journal of Waste Management & Xenobiotics ISSN: 2640-2718. DOI: 10.23880/ajax-16000157.
Liang, S. & Wang, J., 2019, Advanced Remote Sensing: Terrestrial Information Extrac-tion and Applications, Academic Press.
Liaw, A. & Wiener, M., 2002, Classificatiourban-ruralssion by Random Forest, R. news, 2, PP. 18-22.
Mazraeh Talaei, M., Sargargar Ardakani, A. & Sadeghian, S., 2015, Extraction of Unautho-rized Constructions Using UltraCam Aerial Photo and Spatial Information of Yazd City, The Second National Conference on the Application of Advanced Spatial Analysis Models (Remote Sensing and GIS) in land use, Yazd, https://civilica.com/doc/749480.
Mohammadnejad Arouq, V., 2019, Identification of Urban Lands Using Sentinel 1 and 2 Satellite Images Based on the Google Earth Engine (GEE) System, Urban Planning Geography Research, 8(3), PP. 613-630.
Ostankovich, V. & Afanasyev, I., 2018, Illegal Buildings Detection from Satellite Images Using Google Net and Cadastral Map, In 2018 International Conference on Intelligent Systems (IS) (PP. 616-623). IEEE.
Otsu, N., 1979, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1), PP. 62-66.
Pal, M., 2005, Random Forest Classifier for Remote Sensing Classification, International Journal of Remote Sensing, 26, PP. 217-222.
Petit, S., Stasolla, M., Wyard, C., Swinnen, G., Neyt, X. & Hallot, E., 2022, A New Earth Observation Service Based on Sentinel-1 and Sentinel-2 Time Series for the Monitoring of Redevelopment Sites in Wallonia, Belgium, Land, 11(3), P. 360.
Rafieyan, O., Darvishsefat, A. A., Namiranian, M.,2006,  The Area Change Detection in the Northern Forests of Iran Using ETM + Data,  jwss 2006; 10 (3) :277-287 URL: http://jstnar.iut.ac.ir/article-1-584-fa.html
Richards, J.A. & Jia, X., 2006, Remote Sensing Digital Image Analysis: an introduction, Springer Verlag, Berlin.
Ridd, M.K. & Liu, J., 1998, A Comparison of Four Algorithms for Change Detection in an Urban Environment, Remote Sensing of Environment, 63(2), PP. 95-100.
Roudgarmi, P., Khorasani, N.A., Monavari, S.M. & Nouri, J., 2009, Predicting the Effects of Environmental Development Using satellite Images and Remote Sensing Techniques, Journal of Environmental Sciences and Technology, 11(1), PP. 161-172.
Soltani, M.R. & Sargargar Ardakani, A., 2013, Detection of the Boundaries of Buildings Using High Resolution Satellite Images with Object Base Classification Method and Extraction of Unauthorized Constructions, National Conference on the Application of Advanced Spatial Analysis Models (Remote Sensing and GIS) in Aamish The land, Yazd.
Szuster, B.W., Chen, Q. & Borger, M., 2011, Acomparison of Classification Techniques to Support Landcover and Land Use Analysis in Tropical Coastal Zones, Applied Geography, 31(2), PP. 525-532.
Zhang, J. & Foody, G.M., 2001, Fully-Fuzzy Supervised Classification of Sub-Urban Land Cover from Remotely Sensed Imagery: Statistical and Artificial Neuralnetwork Approaches, International Journal of Remote Sensing, 22(4), PP. 615-628.

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