Generating synthetic multispectral images for semantic segmentation in forestry applications

Bittner, D., Ferreira, J.F. ORCID: 0000-0002-2510-2412, Andrada, M.E., Bird, J.J. ORCID: 0000-0002-9858-1231 and Portugal, D., 2022. Generating synthetic multispectral images for semantic segmentation in forestry applications. In: Innovation in Forestry Robotics: Research and Industry Adoption Workshop - IEEE Conference on Robotics and Automation (ICRA 2022), Philadelphia (PA), USA, 23-27 May 2022.

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In this paper, we introduce a GAN-based solution for generating synthetic multispectral images from fully-annotated RGB images for data augmentation purposes in forestry robotics applications at ground-level. Fully-annotated multispectral datasets are difficult to obtain with sufficient training samples when compared to RGB-based datasets, since annotation in this case is often very time-consuming and expensive due to the need for expert knowledge. In this text, a study comparing different GAN-based image translation models designed for data augmentation is presented. Synthetic images generated by the proposed solution are shown to be realistic enough to yield performance ratings comparable to what is obtained using real images.

Item Type: Conference contribution
Description: Poster
Creators: Bittner, D., Ferreira, J.F., Andrada, M.E., Bird, J.J. and Portugal, D.
Date: May 2022
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 07 Jun 2022 15:22
Last Modified: 31 Jan 2023 16:27
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