Plant Species Classification Using Hyperspectral LiDAR with Convolutional Neural Network

Convolutional neural networks (CNN) are capable of extracting features with high accuracy, which is dominant in visual-based classification. Previous researches demonstrate that CNN can extract essential features of the target in the plant feature extraction and classification. Hyperspectral LIDAR (HSL) is a novel active remote sensing technology that can simultaneously collect spectral and spatial information. This paper proposed a novel classification method named VI-CNN for hyperspectral LiDAR, which combines the spectral features with the vegetable index(VI). As far as we know, we are the first to apply CNN to HSL data classification. The VI -CNN is divided into two parts. Firstly, spectral CNN focuses on intra-spectral correlations; secondly, the vegetation indices supplement the biological parameters. The evaluation shows that the concatenation has stronger identification and robustness than standalone methods. The experimental results demonstrate that the VI-CNN significantly improves the classification accuracy against other traditional machine-learning methods.