- Title
- Shrub Detection in High-Resolution Imagery: A Comparative Study of Two Deep Learning Approaches
- Creator
- James, Katherine M F
- Creator
- Bradshaw, Karen L
- Subject
- To be catalogued
- Date Issued
- 2022
- Date
- 2022
- Type
- text
- Type
- book
- Identifier
- http://hdl.handle.net/10962/440326
- Identifier
- vital:73766
- Identifier
- ISBN 9783030955021
- Identifier
- https://doi.org/10.1007/978-3-030-95502-1_41
- Description
- A common task in high-resolution remotely-sensed aerial imagery is the detection of particular target plant species for various ecological and agricultural applications. Although traditionally object-based image analysis approaches have been the most popular method for this task, deep learning approaches such as image patch-based convolutional neural networks (CNNs) have been seen to outperform these older approaches. To a lesser extent, fully convolutional networks (FCNs) that allow for semantic segmentation of images, have also begun to be used in the broader literature. This study investigates patch-based CNNs and FCN-based segmentation for shrub detection, targeting a particular invasive shrub genus. The results show that while a patch-based CNN demonstrates strong performance on ideal image patches, the FCN outperforms this approach on real-world proposed image patches with a 52% higher object-level precision and comparable recall. This indicates that FCN-based segmentation approaches are a promising alternative to patch-based approaches, with the added advantage of not requiring any hand-tuning of a patch proposal algorithm.
- Format
- 17 pages
- Format
- Publisher
- Springer Cham
- Language
- English
- Relation
- James, K., Bradshaw, K. (2022). Shrub Detection in High-Resolution Imagery: A Comparative Study of Two Deep Learning Approaches. In: Garg, D., Jagannathan, S., Gupta, A., Garg, L., Gupta, S. (eds) Advanced Computing. IACC 2021. Communications in Computer and Information Science, vol 1528. Springer, Cham
- Rights
- Authors
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
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Collections
Bradshaw, Karen Lee (Assoc Prof)
RU Department of Computer Science
SDG 15. Life On Land
SDG 09. Industry, Innovation and Infrastructure
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