University of Guilan Caspian Journal of Environmental Sciences 1735-3033 24 2 2026 04 01 Performance evaluation of Artificial Neural Network, Support Vector Machine and Integrated Spectral Indices in satellite image classification 451 464 9183 10.22124/cjes.2025.9183 EN Arash Mesri Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Guilan, Iran Fatemeh Rahimi-Ajdadi Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Guilan, Iran Iraj Bagheri Department of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Guilan, Iran Journal Article 2025 11 06 Satellite remote sensing is effectively used for environmental monitoring and change detection for the sustainable development of human society. While several methods exist for classifying satellite images, relatively few studies have focused on comparing these methods, especially considering the dimensional ratio and spatial distribution of the target phenomena. This study evaluates the performance of three classification methods including ANN, SVM, and an integrated approach that simultaneously uses three spectral indices of NDVI, GNDVI and, NDBI. The overall accuracy and kappa coefficient were calculated from the confusion matrix to statistically evaluate the three methods. Considering that statistical parameters are strongly sensitive to the dispersion and spatial distribution of the test points, a visual comparison was performed by overlaying the classified images with corresponding Google Earth imagery. Comparisons were made for several sample areas, which were categorized based on whether the land uses were integrated or scattered. Based on overall accuracy and kappa coefficient, the methods were ranked as SVM (97.36% and 0.9622), integrated spectral indices (94.06% and 0.9136), and ANN (93.42% and 0.9051). The visual comparison confirmed that SVM provided the best overall performance, consistent with the statistical results. Despite its lower overall accuracy, ANN was found more effective method in narrow areas compared to the other methods. Therefore, ANN is only recommended for detecting land uses with high levels of interference/integration with other features like rivers and roads that are surrounded by some other land uses. https://cjes.guilan.ac.ir/article_9183_ef6ab0c28e5c94045cf2fb9fd906d4ca.pdf