Preservation of sensitive tree species requires timely and accurate information on their distribution in the area under threat. Remote sensing techniques have been increasingly applied as alternatives to costly and time consuming field surveys for assessing forest resources. For this purpose, satellite, aerial and, more recently, Unmanned Aerial Vehicle (UAV) have been the most common platforms used for data collection.
] and hyperspectral [6
] imageries, Light Detection And Ranging (LiDAR) data [8
], and also combinations of them [12
] have been the preferred data source. Clark et al. [6
] used airborne hyperspectral data (161 bands, 437–2434 nm) for the classification of seven tree species. Linear discriminant analysis (LDA), maximum likelihood (ML) and spectral angle mapper (SAM) classifiers were tested. The authors reported accuracy of 88% with a ML classifier based on 60 bands. Dalponte et al. [7
] investigated the use of hyperspectral sensors for the classification of tree species in a boreal forest. Accuracy around 80% was achieved, using Support Vector Machines (SVM) and Random Forest (RF) classifiers.
Immitzer et al. [3
] applied RF to classify 10 tree species in an Austrian temperate forest upon WorldView-2 (8 spectral bands) multispectral data, having achieved an overall classification accuracy around 82%. In a later work, Immitzer et al. [4
] used Sentinel-2 multispectral imagery to classify tree species in Germany with a RF classifier achieving accuracy around 65%. Franklin and Ahmed [5
] reported 78% accuracy in the classification of deciduous tree species applying object-based and machine learning techniques to UAV multispectral images.
Voss and Sugumaran [12
] combined hyperspectral and LiDAR data to classify tree species using an object-oriented approach. Accuracy improvements up to 19% were achieved when both data were combined. Dalponte et al. [15
] investigated the combination of hyperspectral and multispectral images with LiDAR for the classification of tree species in Southern Alps. They achieved 76.5% accuracy in experiments using RF and SVM. Nevalainen et al. [18
] combined UAV-based photogrammetric point clouds and hyperspectral data for tree detection and classification in boreal forests. RF and Multilayer Perceptron (MLP) provided 95% overall accuracy. Berveglieri et al. [19
] developed a method based on multi-temporal Digital Surface Model (DSM) and superpixels for classifying successional stages and their evolution in tropical forest remnants in Brazil.
While numerous studies have been conducted on multispectral, hyperspectral, LiDAR and combinations of them, there are few studies relying on RGB images for tree detection/classification. Feng et al. [20
] used RGB images for urban vegetation mapping. They used RF classifiers, and verified that the texture, derived from the RGB images, contributed significantly to improve the classification accuracy. However, tree species classification was not specifically addressed in this work.
In the last few years, approaches based on deep learning, such as Convolutional Neural Networks (CNNs) and their variants, gained popularity in many fields, including remote sensing data analysis. Mizoguchi et al. [11
] applied CNN to terrestrial LiDAR data to classify tree species and achieved between 85% and 90% accuracy. Weinstein et al. [21
] used semi-supervised deep learning neural networks for individual tree-crown detection in RGB airborne imagery. Barré et al. [22
] developed a deep learning system for classifying plant species based on leaf images using CNN.
Regarding plant species classification and diseases detection based on leaf images, several works were developed [23
]. Fuentes et al. [25
] focused on the development of a deep-learning-based detector for real-time tomato plant diseases and pests recognition, considering three CNNs: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN) and Single Shot Multibox Detector (SSD). However, tree detection was not the target application.
To the best of our knowledge, no study focused thus far on state-of-the-art deep learning-based methods for tree detection on images generated by RGB cameras on board of UAVs. The present study addressed this gap and presented an evaluation of deep learning-based methods for individual tree detection on UAV/RGB high resolution imagery. This study focused on a tree species known as Dipteryx alata
Vogel (Fabaceae), popularly known as baru or cumbaru (henceforth cumbaru), which is threatened by extinction according to the IUCN (2019) (The International Union for Conservation of Nature’s Red List of Threatened Species, https://www.iucnredlist.org/species/32984/9741012
). This species has a high economic potential and a wide range of applications, mainly for the use of non-timber forest products. It is distributed over a large territory, being mostly associated to the Brazilian Savanna, although it also occurs in the wetlands [29
] in midwest Brazil.
Our work hypothesis is that state-of-the-art deep learning-based methods are able to detect single tree species upon high-resolution RGB images with attractive cost, accuracy and computational load. The contribution of this work is twofold. First, we assessed the usage of high-resolution images produced by RGB cameras carried by UAVs for individual trees detection. Second, we compared three state-of-the-art CNN-based object detection methods, namely FasterRCNN, RetinaNet and YOLOv3, for the detection of cumbaru trees on said UAV/RGB imagery.
The rest of this paper is organized as follows. Section 2
presents the materials and methods adopted in this study. Section 3
presents and discusses the results obtained in the experimental analysis. Finally, Section 4
summarizes the main conclusions and points to future directions.
In this work, we proposed and evaluated an approach for the detection of tree species based on CNN and high resolution images captured by RGB cameras in an UAV platform. Three state-of-the-art CNN-based methods for object detection were tested: Faster R-CNN, YOLOv3 and RetinaNet. In the experiments carried out on a dataset comprising 392 images, RetinaNet achieved the most accurate results, having delivered 92.64% average precision. Regarding computational cost, YOLOv3 was faster than its counterparts. Faster RCNN was the least accurate and at the same time the most computationally demanding among the assessed detection methods.
The experimental results indicate that RGB cameras attached to UAVs and CNN-based detection algorithms constitute a promising approach towards the development of operational tools for population estimates of tree species, as well for demography monitoring, which is fundamental to integrate economic development and nature conservation. Future works will investigate the application of the proposed techniques considering other tree species. Real-time tree detection using embedded devices will also be investigated.