Remote Sensing and GIS Applications in Environmental Sciences

Comparison of the efficiency of pixel-based support vector machine kernel functions and object-based fuzzy operators in monitoring the changes in urban growth and expansion of Tabriz.

Document Type : Original Article

Authors

1 ph.D student of remote sensing and geographic information system of Tabriz University

2 Department of Mapping, Technical Faculty, Tabriz University

3 Ph.D Student. Acecr Researcher: Development and Planning Research Institute, GIS & RS Center, Tabriz

10.22034/rsgi.2025.63373.1098

Abstract

Urban growth as a determining factor of social welfare and environmental sustainability is considered very vital. In recent years, remote sensing data has been used as a tool to determine the size of urban expansion and monitor urban growth. The purpose of the present research is to compare the efficiency of pixel-based vector machine kernel functions and object-oriented fuzzy operators in monitoring Tabriz urban growth changes using Sentinel 2 satellite. in order to classify each image based on pixel base support vector machine kernel functions, using support vector machine kernels, the image classification process was performed and the urban growth map of each function was produced for the years under study. ecognition software was used for object-oriented classification. At this stage, segmentation was done based on different scales, shape factor and compression ratio to reach the optimal segmentation mode. Then, teaching points were identified and classification was done using fuzzy operators. Based on the results of this research, the AND fuzzy operator with an overall accuracy of 96.49% and a kappa coefficient of 0.9688 for the image of 2014 and an overall accuracy of 97.31% and a kappa coefficient of 0.9725 for the image of 1403, the accuracy value provided more. Therefore, considering the greater accuracy of object-oriented fuzzy operators , it can be stated that object-oriented processing algorithms of satellite images in the classification of digital satellite images, compared to support vector machine algorithms, make it possible to achieve higher accuracy in extracting the urban area of ​​Tabriz.

Keywords

Main Subjects

رشد شهری به عنوان یک عامل تعیین کننده رفاه اجتماعی و پایداری محیطی، بسیار حیاتی به شمار می­آید. در سا­ل های اخیر داده­های سنجش از دور به دلیل داشتن پوشش فضایی مناسب برای مناطق شهری، به عنوان ابزاری منحصر به فرد برای تعیین اندازه گسترش شهری و نظارت بر رشد شهری مورد استفاده قرار گرفته است. هدف از پژوهش حاضر، مقایسه­ کارآیی توابع کرنل ماشین­بردار پشتیبان پیکسل­پایه و عملگرهای فازی شئ­گرا در پایش تغییرات رشد شهری تبریز با استفاده از تصاویر سری زمانی سال­های 1394 و 1403 ماهواره سنتینل 2 می­باشد. در این راستا، جهت طبقه­بندی هر تصویر بر اساس توابع کرنل ماشین­بردار پشتیبان پیکسل­پایه، در نرم­افزار ENVI با استفاده از کرنل­های خطی، چندجمله­ای، پایه شعاعی و سیگموئید، فرایند طبقه­بندی تصاویر انجام شد و نقشه رشد شهری هر کدام از توابع برای سال­های مورد مطالعه تولید شد. از نرم­افزار ایکاگنیشن جهت قطعه­بندی و طبقه­بندی مبتنی بر عملگر­های فازی شئ­گرا استفاده گردید. در این مرحله اقدام به قطعه­بندی بر اساس مقیاس­ها، ضریب شکل­ها و ضریب فشردگی­های مختلف جهت رسیدن به حالت قطعه­بندی بهینه گردید. سپس، نقاط تعلیماتی مشخص گردیدند و با استفاده از عملگر­های فازی AND، OR، MGE، MAR، MGWE و ALP طبقه­بندی انجام گردید. با استفاده از نقاط نمونه واقعیت زمینی، ارزیابی دقت برای کلیه نقشه­های تولیدی انجام شد. بر اساس نتایج این پژوهش، عملگر فازی AND با دقت کلی 49/96 درصد و ضریب کاپای 9688/0 برای تصویر طبقه­بندی شده سال 1394 و دقت کلی 31/97 درصد و ضریب کاپای 9725/0 برای تصویر طبقه­بندی شده سال 1403، مقدار دقت بیشتری را ارائه نمودند. لذا با توجه به دقت بیشتر عملگرهای فازی شئ­گرا در این پژوهش، می­توان ابراز نمود که الگوریتم­های پردازش شئ­گرای تصاویر ماهواره­ای در طبقه­بندی تصاویر رقومی ماهواره­ای در مقایسه با الگوریتم­های ماشین­بردار پشتیبان، دست­یابی به دقّت بالاتر را در استخراج محدوده شهری کلانشهر تبریز امکان­پذیر می­س

کلمات کلیدی: ماشین بردار پشتیبان، پردازش شئ­گرای تصاویر ماهواره­ای، فازی، سنتیل2، تبریز

Ali, A., Dunlop, P., Coleman, S., Kerr, D., McNabb, R. W., & Noormets, R. (2023). Glacier area changes in Novaya Zemlya from 1986–89 to 2019–21 using object-based image analysis in Google Earth Engine. Journal of Glaciology, 1-12. https://doi.org/10.1017/jog.2023.18
Arabi Aliabad, F., Malamiri, H. R. G., Shojaei, S., Sarsangi, A., Ferreira, C. S. S., & Kalantari, Z. (2022). Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2. Remote Sensing, 14(13), 3227. https://www.mdpi.com/2072-4292/14/13/3227
Behnia, N., Zare, M., Moosavi, V., & Khajeddin, S. I. (2020). Evaluation of a Hierarchical Classification Method and Statistical Comparison with Pixel-Based and Object-Oriented Approaches. ECOPERSIA, 8(4), 209-219. http://ecopersia.modares.ac.ir/article-24-38774-en.html
http://ecopersia.modares.ac.ir/article-24-38774-en.pdf
Cetin, M. (2009). A satellite based assessment of the impact of urban expansion around a lagoon. International Journal of Environmental Science & Technology, 6(4), 579-590. https://doi.org/10.1007/BF03326098
Dhanaraj, K., & Angadi, D. P. (2022). Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India. GeoJournal, 87(2), 1133-1159. https://doi.org/10.1007/s10708-020-10302-4
Feizizadeh, B., Blaschke, T., Tiede, D., & Moghaddam, M. H. R. (2017). Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology, 293, 240-254. https://doi.org/https://doi.org/10.1016/j.geomorph.2017.06.002
Fu, X., Zhou, W., Zhou, X., & Hu, Y. (2023). Crop Mapping and Spatio–Temporal Analysis in Valley Areas Using Object-Oriented Machine Learning Methods Combined with Feature Optimization. Agronomy, 13(10), 2467. https://www.mdpi.com/2073-4395/13/10/2467
Hartoni, Siregar, V., Wouthuyzen, S., & Agus, S. (2022). Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu, Indonesia. Biodiversitas Journal of Biological Diversity, 23. https://doi.org/10.13057/biodiv/d230155
Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), 117-124. https://doi.org/https://doi.org/10.1016/j.ijsbe.2015.02.005
Herold, M., Scepan, J., & Clarke, K. C. (2002). The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses. Environment and Planning A: Economy and Space, 34(8), 1443-1458. https://doi.org/10.1068/a3496
Kazemi Garajeh, M., Feizizadeh, B., Weng, Q., Rezaei Moghaddam, M. H., & Kazemi Garajeh, A. (2022). Desert landform detection and mapping using a semi-automated object-based image analysis approach. Journal of Arid Environments, 199, 104721. https://doi.org/https://doi.org/10.1016/j.jaridenv.2022.104721
Li, Q., Mou, L., Sun, Y., Hua, Y., Shi, Y., & Zhu, X. X. (2024). A Review of Building Extraction From Remote Sensing Imagery: Geometrical Structures and Semantic Attributes. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-15. https://doi.org/10.1109/TGRS.2024.3369723
Najafi, P., Navid, H., Feizizadeh, B., Eskandari, I., & Blaschke, T. (2019). Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue. Remote Sensing, 11(21), 2583. https://www.mdpi.com/2072-4292/11/21/2583
Saraf, N. M., Lokman, M. F., Abdul Rasam, A. R., & Hashim, N. (2022). Assessment of urban growth changes in Klang District using Support Vector Machine by different kernel. IOP Conference Series: Earth and Environmental Science, 1051(1), 012023. https://doi.org/10.1088/1755-1315/1051/1/012023
Shehu, P., Rikko, L. S., & Azi, M. B. (2023). Monitoring urban growth and changes in land use and land cover: a strategy for sustainable urban development. International Journal of Human Capital in Urban Management, 8(1), 111-126. https://doi.org/10.22034/ijhcum.2023.01.09
Sobieraj, J., Marín, M. F., & Metelski, D. (2023). Mapping of impervious surfaces with the use of remote sensing imagery: Support Vector Machines classification and GIS-based approach. Archives of Civil Engineering, vol. 69(No 3), 129-146. https://doi.org/10.24425/ace.2023.146071
Stilla, U., & Xu, Y. (2023). Change detection of urban objects using 3D point clouds: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 197, 228-255. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2023.01.010
Tariq, A., Jiango, Y., Li, Q., Gao, J., Lu, L., Soufan, W., Almutairi, K. F., & Habib-ur-Rahman, M. (2023). Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon, 9(2), e13212. https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e13212
Uca Avcı, Z., Karaman, M., Ozelkan, E., & Papila, I. (2011). A Comparison of Pixel-Based and Object-Based Classification Methods, A Case Study: Istanbul, Turkey 34th International Symposium on Remote Sensing of Environment, Sydney, Australia.
Wu, J., Lin, L., Zhang, C., Li, T., Cheng, X., & Nan, F. (2023). Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 16-31. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2022.12.017
Yang, K., Ye, Z., Liu, H., Su, X., Yu, C., Zhang, H., & Lai, R. (2023). A new framework for GEOBIA: accurate individual plant extraction and detection using high-resolution RGB data from UAVs. International Journal of Digital Earth, 16(1), 2599-2622. https://doi.org/10.1080/17538947.2023.2233484
Yasin, M. Y., Abdullah, J., Noor, N. M., Yusoff, M. M., & Noor, N. M. (2022). Landsat observation of urban growth and land use change using NDVI and NDBI analysis. IOP Conference Series: Earth and Environmental Science, 1067(1), 012037. https://doi.org/10.1088/1755-1315/1067/1/012037
Ye, Z., Yang, K., Lin, Y., Guo, S., Sun, Y., Chen, X., Lai, R., & Zhang, H. (2023). A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images. Computers and Electronics in Agriculture, 209, 107822. https://doi.org/https://doi.org/10.1016/j.compag.2023.107822
Zhu, Q., Guo, X., Li, Z., & Li, D. (2022). A review of multi-class change detection for satellite remote sensing imagery. Geo-spatial Information Science, 1-15. https://doi.org/10.1080/10095020.2022.2128902
Remote Sensing and GIS Applications in Environmental Sciences
Volume 5, Issue 14
April 2025
Pages 20-1
  • Receive Date: 07 September 2024
  • Revise Date: 14 January 2025
  • Accept Date: 29 January 2025