Investigation of the performances of support vector machine, random forest, and 3D-2D convolutional neural network for hyperspectral image classification

Abstract

Classification of the hyperspectral images (HSIs) is one of the most challenging tasks hyperspectral remote sensing. Various Machine Learning classification algorithms have been implemented to HSI classification. In recent years, several Convolutional Neural Network (CNN) architectures were developed for HSI classification. The aim of this study is to test the performance of CNN, and well-known Support Vector Machine and Random Forest algorithms using the HyRANK Loukia, Houston 2013, and Salinas Scene datasets. The findings indicate that the Modified HybridSN CNN outperformed other algorithms across all datasets, as demonstrated by various performance evaluation metrics.