Object-Based Hyperspectral Classification Approach to Tree Species by 3D-CNN

Abstract

This article focuses on the design and implementation of a 3D Convolutional Neural Network (3D-CNN) for hyperspectral classification of tree species. Real data from repeated aerial imaging of selected sections of the Slovak electricity transmission system was used to test the models, using technology from our partner VUJE, a.s., which has a hyperspectral scanner consisting of two cameras, HySpex VNIR-1800 and HySpex SWIR-384, capturing wavelengths from 400 to 2500 nm. The results of 3D-CNN classification based on autumn and spring data collection were compared, as well as classification after fusing data from selected areas for the purpose of comparing object vs. pixel classification. The presented object-based classification model based on 3D-CNN achieves on average 9% better classification accuracy compared to pixel-based classification using 1D Convolutional Neural Network (1D-CNN).