Geography and Human Relationships

Geography and Human Relationships

Using machine learning algorithms based on object-oriented processing of satellite images to improve spatial planning in the physical development of the suburbs of Meshgin Shahr

Document Type : Original Article

Authors
1 University of Tabriz, Faculty of Planning and Environmental Sciences, Professor Department of Remote Sensing and Geographic Information System, Tabriz, Iran
2 Remote Sensing and Geographical Information System - Faculty of Planning and Environmental Sciences - Tabriz University - Tabriz - Iran / Researcher of Acecr
10.22034/gahr.2025.460156.2150
Abstract
Due to the unprecedented trend of population growth and the expansion of cities and with the increase in construction and in the border, the urban periphery has overshadowed the issue of the management system and urban planning program; one of the new approaches in urban planning is the use of images. satellite and new methods of remote sensing. The method of the current research is descriptive and quantitative, which was carried out with the aim of using machine learning algorithms based on object-oriented processing of Landsat and Sentinel series satellite images. After correcting and combining images or Image fusion using NNDiffuse and Gram schmidt methods, Landsat and Sentinel images were performed in ENVI 5.6 software and a new image with the common characteristics of the two images was produced for segmentation and classification of the software. ecognition was used and the segmentation process was carried out based on the appropriate scale, shape factor and compression factor with the aim of producing visual objects. Using machine learning classifiers based on object-oriented processing, the physical area of Meshgin shahr city was produced. According to the research results, the Bayes classifier has an overall accuracy of 96% and a Kappa coefficient of 0.94, k - the closest Neighbor with overall accuracy of 97% and Kappa coefficient of 0.94, support vector machine with overall accuracy of 95% and Kappa coefficient of 0.94, decision tree with overall accuracy of 96% and Kappa coefficient of 0.94 and random trees with accuracy The total was 94% and the Kappa coefficient was 0.84. Therefore, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and Kappa coefficient of 0.94 provided more accuracy. The built-up land class has changed the most by 16.92% in terms of area increase.
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Volume 8, Issue 3 - Serial Number 31
Autumn 2025
Pages 209-230

  • Receive Date 29 May 2024
  • Revise Date 15 July 2025
  • Accept Date 26 July 2025