Generating synthetic multispectral images for semantic segmentation in forestry applications

Bittner, D, Ferreira, JF ORCID logoORCID: https://orcid.org/0000-0002-2510-2412, Andrada, ME, Bird, JJ ORCID logoORCID: https://orcid.org/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.

[thumbnail of 1551501_Ferreira.pdf]
Preview
Text
1551501_Ferreira.pdf - Post-print

Download (8MB) | Preview

Abstract

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
Identifiers:
Number
Type
1551501
Other
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
Related URLs:
URI: https://irep.ntu.ac.uk/id/eprint/46416

Actions (login required)

Edit View Edit View

Statistics

Views

Views per month over past year

Downloads

Downloads per month over past year