Paper Info
Title
Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities
Abstract
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 (approximate to 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 (approximate to 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.
Year
DOI
Venue
2020
10.3390/rs12162602
REMOTE SENSING
Keywords
DocType
Volume
semantic segmentation,machine learning,random forest,deep learning,CNN
Journal
12
Issue
Citations 
PageRank 
16
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Saheba Bhatnagar100.34
Laurence W. Gill201.35
Bidisha Ghosh3859.13