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Article

Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities

Department of Civil, Structural and Environmental Engineering, Trinity College, University of Dublin, D02 PN40 Dublin, Ireland
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Remote Sens. 2020, 12(16), 2602; https://doi.org/10.3390/rs12162602
Received: 12 June 2020 / Revised: 8 August 2020 / Accepted: 11 August 2020 / Published: 12 August 2020
(This article belongs to the Special Issue UAVs for Vegetation Monitoring)
The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice. View Full-Text
Keywords: semantic segmentation; machine learning; random forest; deep learning; CNN semantic segmentation; machine learning; random forest; deep learning; CNN
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MDPI and ACS Style

Bhatnagar, S.; Gill, L.; Ghosh, B. Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities. Remote Sens. 2020, 12, 2602. https://doi.org/10.3390/rs12162602

AMA Style

Bhatnagar S, Gill L, Ghosh B. Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities. Remote Sensing. 2020; 12(16):2602. https://doi.org/10.3390/rs12162602

Chicago/Turabian Style

Bhatnagar, Saheba, Laurence Gill, and Bidisha Ghosh. 2020. "Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities" Remote Sensing 12, no. 16: 2602. https://doi.org/10.3390/rs12162602

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