The PCA and 1D-CNN Dimension Reduction Comparison for Hyperspectral Classification of Tree Species

This paper is focused on the comparison of two feature space dimensionality reduction approaches, namely Principal Component Analysis and One-Dimensional Convolutional Neural Network to identify the hyperspectral classification model with the highest classification accuracy. The reference model is an enhanced multilayer perceptron without using dimension reduction. It differs from the standard multilayer perceptron by adding dropout layers, which prevent overfitting and stabilize the training process. The main idea of choosing a One-Dimensional Convolutional Neural Network approach was to leverage its capability to reduce the feature space of the input data using a form of supervised learning. This way, it was possible to extract class-relevant features of the input data at a pixel level. The results show that the kappa score and the accuracy of the proposed model were 0.828 and 0.803 without dimension reduction, 0.863 and 0.843 with Principal Component Analysis, and for One-Dimensional Convolutional Neural Network 0.847 and 0.825, respectively.