Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification

Game, Chloe A., Thompson, Michael B. and Finlayson, Graham D. (2024) Machine learning for non-experts: A more accessible and simpler approach to automatic benthic habitat classification. Ecological Informatics, 81. ISSN 1574-9541

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Abstract

Automating identification of benthic habitats from imagery, with Machine Learning (ML), is necessary to contribute efficiently and effectively to marine spatial planning. A promising method is to adapt pre-trained general convolutional neural networks (CNNs) to a new classification task (transfer learning). However, this is often inaccessible to a non-specialist, requiring large investments in computational resources and time (for user comprehension and model training). In this paper, we demonstrate a simpler transfer learning framework for classifying broad deep-sea benthic habitats. Specifically, we take an ‘off-the-shelf’ CNN (VGG16) and use it to extract features (pixel patterns) from benthic images (without further training). The default outputs of VGG16 are then fed in to a Support Vector Machine (SVM), a classical and simpler method than deep networks. For comparison, we also train the remaining classification layers of VGG16 using stochastic gradient descent. The discriminative power of these approaches is demonstrated on three benthic datasets (574–8353 images) from Norwegian waters; each using a unique imaging platform. Benthic habitats are broadly classified as Soft Substrate (sands, muds), Hard Substrate (gravels, cobbles and boulders) and Reef (Desmophyllum pertusum). We found that the relatively simplicity of the SVM classifier did not compromise performance. Results were competitive with the CNN classifier and consistently high, with test accuracy ranging from 0.87 to 0.95 (average = 0.9 (±0.04)) across datasets, somewhat increasing with dataset size. Impressively, these results were achieved 2.4–5× faster than CNN training and had significantly less dependency on high-specification hardware. Our suggested approach maximises conceptual and practical simplicity, representing a realistic baseline for novice users when approaching benthic habitat classification. This method has wide potential. It allows automated image grouping to aid annotation or further model selection, as well as screening of old-datasets. It is especially suited to offshore scenarios as it can provide quick, albeit crude, insights into habitat presence, allowing adaptation of sampling protocols in near real-time.

Item Type:	Article
Additional Information:	Funding Information: This work was supported by NERC and EPSRC grants: NE/N012070/1 and EP/S028730/1, respectively.
Uncontrolled Keywords:	benthic habitats,computer vision,convolutional neural network,machine learning,support vector machine,user-friendly,ecology, evolution, behavior and systematics,ecology,modelling and simulation,ecological modelling,computer science applications,computational theory and mathematics,applied mathematics ,/dk/atira/pure/subjectarea/asjc/1100/1105
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Colour and Imaging Lab
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	14 May 2024 11:31
Last Modified:	25 Sep 2024 17:49
URI:	https://ueaeprints.uea.ac.uk/id/eprint/95191
DOI:	10.1016/j.ecoinf.2024.102619

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