Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Abstract

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, bio-mass estimation, etc. Deep Neural Networks (DNN) have shown superior results when comparing with conventional machine learning methods such as Multi-Layer Perceptron (MLP) in cases of huge input data. The objective of this research was to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, RGB channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

The Digital Forest: Mapping a Decade of Knowledge on Technological Applications for Forest Ecosystems

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Exploitation of Time Series Sentinel-2 Data and Different Machine Learning Algorithms for Detailed Tree Species Classification

“…In machine learning, the selection of hyper-parameter is important for model optimization and accuracy improvement [19]. Compared with other parameters obtained through training, the values of the hyper-parameter are used to control the learning process [18], [52]. Hyper-parameters of CNN include the number of hidden layers, kernel size, and dropout rate.…”

Piecewise Anomaly Detection Using Minimal Learning Machine for Hyperspectral Images

“…The tree species are mainly spruces, pines and birches. The dataset was originally captured for the tree species classification purposes (Nevalainen et al, 2017, Nezami et al, 2020, Polonen et al, 2018 The details of the dataset are described in (Nevalainen et al, 2017). The forest dataset consists of orthophoto mosaic images of the area (Honkavaara et al, 2013).…”