Iranian Journal of Forest

Iranian Journal of Forest

Evaluation of the efficiency of supervised algorithms in preparing the ground cover map using Sentinel 2 images in the Zagros Forest Habitat

Document Type : Research Paper

Authors
F Rashidi
A Jaafari
Assistant Prof., Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
10.22034/ijf.2023.328404.1849
Abstract
Introduction:The aim of this study is to investigate the performance of various algorithms in enhancing the accuracy of land cover maps using Sentinel images.
Material and Methods: The study area is a sheet (1:25000) encompassing an area of 15782.6 hectares. The land cover map was created using a range of algorithms - Mahalanobis distance, maximum likelihood, minimum distance, neural network, parallelepiped, support vector machine, spectral angle mapper, spectral information divergence, and binary encoding - applied to the optimal band composition derived from 12 bands (2, 3, 4, 5, 6, 7, 8, 8a, 11, 12, NDVI, SAVI). The training area was obtained from field information and satellite images. 70% of the samples were used for classification and 30% for evaluating the accuracy of the classified maps.
Findings:The results indicate that the support vector machine, neural network, and maximum likelihood classifications have the highest accuracy. The support vector machine classifier is slightly more accurate than the other two classifiers. To improve the classification, support vector machine kernels (linear, polynomial, radial basis function, and sigmoid), spectral angle mapper settings (6 modes), and parallelepiped (2 modes) were utilized. The results show that the support vector machine classifier, using the 6th degree polynomial function method, has the highest accuracy. Following this, the map was post-processed with the highest accuracy, resulting in an overall accuracy of 96.63 and a kappa coefficient of 0.9393.
Conclusion: An examination of 21 classification modes on Sentinel images in this study provides a comprehensive review of classification algorithms compared to previous studies.
Keywords
kernel
Maximum likelihood
Neural network
Polynomial
Support vector machine
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  • Receive Date 03 July 2024