(untitled) (Mongolian Journal of Geography and Geoecology, 2025)
The aim of this study is to compare the performance of deep learning, machine learning, and advanced hyperspectral image classification methods for distinguishing land cover types in Ulaanbaatar city. The study area includes various land cover classes such as built-up areas, ger districts, forests, willows, grasslands, soil, and water, with significant statistical overlaps between the built-up areas and the ger districts. For data sources, we selected PRISMA (Hyperspectral Precursor of the Application Mission) and Sentinel-1B dual-polarization synthetic aperture radar (SAR) images. Three different band combinations were utilized to identify the mixed urban land cover classes in Mongolia’s capital city. To differentiate the existing classes, we employed an artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM), assessing their performance against one another. To evaluate the accuracy of the classification results, we applied the Kappa coefficient. For all three band combinations, the SVM method demonstrated superior performance, with Kappa coefficients ranging from 0.96 to 0.98. The ANN showed the second-highest performance, with Kappa coefficients ranging from 0.83 to 0.96. In contrast, the SAM yielded the lowest performance, with Kappa coefficients between 0.67 and 0.71. Our study observed that the performance of the selected classification techniques depended on the chosen parameters and the structure of the datasets. Overall, this study highlights that the combined use of hyperspectral and microwave datasets can enhance the classification of land cover types, with the SVM approach emerging as the most reliable method for producing an accurate land cover map.