Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and comp...

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Bibliographic Details
Main Authors: Daliana Lobo Torres, Raul Queiroz Feitosa, Patrick Nigri Happ, Laura Elena Cué La Rosa, José Marcato Junior, José Martins, Patrik Olã Bressan, Wesley Nunes Gonçalves, Veraldo Liesenberg
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sensors
Subjects:
deep learning
fully convolution neural networks
semantic segmentation
unmanned aerial vehicle (uav)
Online Access:https://www.mdpi.com/1424-8220/20/2/563

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