Deep learning model for accurate vegetation classification using RGB image only

Bulent Ayhan; Chiman Kwan; Jude Larkin; Liyun Kwan; Dimitrios Skarlatos; Marinos Vlachos

doi:10.1117/12.2557833

21 April 2020 Deep learning model for accurate vegetation classification using RGB image only

Bulent Ayhan, Chiman Kwan, Jude Larkin, Liyun Kwan, Dimitrios Skarlatos, Marinos Vlachos

Proceedings Volume 11398, Geospatial Informatics X; 113980H (2020) https://doi.org/10.1117/12.2557833
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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.

Citation Download Citation

Bulent Ayhan, Chiman Kwan, Jude Larkin, Liyun Kwan, Dimitrios Skarlatos, and Marinos Vlachos "Deep learning model for accurate vegetation classification using RGB image only", Proc. SPIE 11398, Geospatial Informatics X, 113980H (21 April 2020); https://doi.org/10.1117/12.2557833

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