Contribution to Pixel-Based Hyperspectral Classification of Tree Species by 1D-CNN

This paper focuses on the design and implementation of a one-dimensional convolutional neural network model in hyperspectral tree species data classification. The proposed approach involves dimensionality reduction in the form of feature extraction using principal component analysis. Both the number of components, which influences the dimensionality of feature space and explained variance, which indicates the proportion of data variability captured were selected as the criterion for feature extraction. As a result, this work compares the classification accuracy and kappa scores of the proposed model for data without using principal component analysis and for data using principal component analysis with different number of transformed input features depending on the criterion used.