Abstract
Modern remote sensing technologies provide a wide range of data, with hyperspectral (HSI) and multispectral (MSI) images being of particular importance. This paper presents a comparative analysis of machine learning (ML) methods used for processing HSI and MSI data. The main objective of the study is to assess the efficiency of various ML algorithms in classification, prediction, and dimensionality reduction tasks. The methodology includes traditional algorithms such as Support Vector Machines (SVM) and Random Forest, as well as modern neural network architectures, including Convolutional Neural Networks (CNN) and autoencoders. The advantages and limitations of ML methods are analyzed depending on the type of input data and the target task. The obtained results show that hyperspectral data require more powerful computational methods, whereas multispectral data allow achieving acceptable accuracy using less complex algorithms. The study highlights the importance of integrating modern ML methods into remote sensing data processing, which contributes to the development of automated systems for analyzing and interpreting remote sensing imagery