Abstract
Abstract. The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced feature extraction and classification methods. Different from the traditional feature extraction methods, that highly depend on user’s knowledge, the convolutional neural network (CNN)-based method can automatically learn and extract the spatial-related features layer by layer. However, in order to capture significant features of the data, the CNN classifier requires a large number of training samples, which are hardly available when dealing with tree species in tropical forests. This study investigated the following topics concerning the classification of 14 tree species in a subtropical forest area of Southern Brazil: i) the performance of the CNN method associated with a previous step to increase and balance the sample set (data augmentation) for tree species classification as compared to the conventional machine learning methods support vector machine (SVM) and random forest (RF) using the original training data; ii) the performance of the SVM and RF classifiers when associated with a data augmentation step and spatial features extracted from a CNN. Results showed that the CNN classifier outperformed the conventional SVM and RF classifiers, reaching an overall accuracy (OA) of 84.37% and Kappa of 0.82. The SVM and RF had a poor accuracy with the original spectral bands (OA 62.67% and 59.24%) but presented an increase between 14% and 21% in OA when associated with a data augmentation and spatial features extracted from a CNN.
Highlights
One of the major challenges for conservation is to obtain reliable and accurate information at a large scale to monitor biodiversity, resources as well as the human impact on natural ecosystems (Wagner et al, 2019)
The potential use of a data augmentation and deep spatial features extracted from a convolutional neural network (CNN) and incorporated into the support vector machine (SVM) and random forest (RF) classifiers was evaluated
The SVM and RF had a poor accuracy when only the original spectral bands were used (OA 62.67% and 59.24%), but an increase between 14% and 21% for SVM and RF, respectively, was observed when a data augmentation process and deep features extracted from the CNN were employed by these classifiers
Summary
One of the major challenges for conservation is to obtain reliable and accurate information at a large scale to monitor biodiversity, resources as well as the human impact on natural ecosystems (Wagner et al, 2019). UAV-borne sensors enable to collect data even under cloud cover conditions They are flexible regarding spatial and temporal resolution, what makes them a cost-effective and operational solution for tree species classification (Nevalainen et al, 2017; Tuominen et al, 2018; Sothe et al, 2019; Miyoshi et al, 2020). The gray-level co-occurrence matrix (GLCM), for instance, is frequently applied for tree species classification (Franklin, Ahmed, 2017; Maschler et al, 2018; Ferreira et al, 2019; Sothe et al, 2019) Such techniques commonly require predefined spatial filter and other parameters which are subjectively determined by the user according to his/her knowledge of the problem. These spatial features are aim-specific ones, which means that only one specific type of objects can be detected by each parameter configuration, making it impossible to describe all types of objects by setting empirical parameters (Zhao, Du, 2016)
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