Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs.

Anderson Aparecido Dos Santos,Camila Aoki,Márcio Santos Araújo,Raul Queiroz Feitosa,Veraldo Liesenberg,Hemerson Pistori,José Marcato Junior,Edson Takashi Matsubara,Anette Eltner,Henrique Lopes Siqueira,David Robledo Di Martini,Wesley Nunes Gonçalves,Everton Castelão Tetila

doi:10.3390/s19163595

Abstract

Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.

Highlights

Preservation of sensitive tree species requires timely and accurate information on their distribution in the area under threat
Despite the comparatively smaller Intersection over Union (IoU), Faster-RCNN and YOLOv3 achieved encouraging results considering the complexity of the problem, as the dataset contains many similar trees
RetinaNet consistently achieved the best results on all folds

Summary

Introduction

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. Multispectral [1,2,3,4,5] and hyperspectral [6,7] imageries, Light Detection And Ranging (LiDAR). Used airborne hyperspectral data (161 bands, 437–2434 nm) for the classification of seven tree species.

Methods

Results

Discussion

Conclusion