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Title: Deep learning model for accurate vegetation classification using RGB image only
Authors: Ayhan, Bulent 
Kwan, Chiman 
Larkin, Jude 
Kwan, Liyun 
Skarlatos, Dimitrios 
Vlachos, Marinos 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Deep Learning;DSM;DTM;Image inpainting;Vegetation extraction
Issue Date: 21-Apr-2020
Source: Geospatial Informatics X, 27 April - 8 May 2020, United States
Conference: Geospatial Informatics 
Abstract: The objective of this paper is to detect the type of vegetation so that a more accurate Digital Terrain Model (DTM) can be generated by excluding the vegetation from the Digital Surface Model (DSM) based on the vegetation type (such as trees). This way, many different inpainting methods can be applied subsequently to restore the terrain information from the removed vegetation pixels from DSM and obtain a more accurate DTM. We trained three DeepLabV3+ models with three different datasets that are collected at different resolutions. Among the three DeepLabV3+ models, the model trained with the dataset that has an image resolution close to the test data images provided the best performance and the semantic segmentation results with this model looked highly promising.
ISBN: 978-151063573-9
DOI: 10.1117/12.2557833
Rights: © SPIE
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Conference Papers
Affiliation : Applied Research LLC 
Cyprus University of Technology 
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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