Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification

Abstract

Abstract This study aims to evaluate the performance of state-of-the-art HybridSN deep learning algorithm versus standard machine learning (ML) and deep learning (DL) techniques using open-source Python libraries for producing hyperspectral land use and land cover (LULC) classification maps. Japanese Chikusei hyperspectral datasets captured by the airborne platform using Hyperspec-VNIR-C sensor were used in this study. Standard ML methods used in this study were support vector machine linear kernel (SVM-linear), support vector machine radial basis function kernel (SVM-RBF) and random forests (RFs) that were provided in Python’s Scikit-learn library. DL techniques used in this study were multilayer perceptron (MLP), two-dimensional convolutional neural network (2-D CNN) and hybrid spectral convolutional neural network (HybridSN), which integrates the 2-D and 3-D feature learning. These DL models were built based on the sequential model using Keras API. The results show that all the proposed methods obtained overall accuracies (OAs) above 95%. The HybridSN and 2-D CNN models gave the best score with 99.97% OAs for hyperspectral image classification using the Chikusei dataset.