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Deep fusion net for coral classification in fluorescence and reflectance images

Nadeem, U., Bennamoun, M., Sohel, F. and Togneri, R. (2019) Deep fusion net for coral classification in fluorescence and reflectance images. In: Digital Image Computing: Techniques and Applications (DICTA) 2019, 2 - 4 December 2019, Hyatt Regency Perth, Australia

Link to Published Version: https://doi.org/10.1109/DICTA47822.2019.8945925
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Abstract

Coral reefs are vital for marine ecosystem and fishing industry. Automatic classification of corals is essential for the preservation and study of coral reefs. However, significant intra-class variations and inter-class similarity among coral genera, as well as the challenges of underwater illumination present a great hindrance for the automatic classification. We propose an end-to-end trainable Deep Fusion Net for the classification of corals from two types of images. The network takes two simultaneous inputs of reflectance and fluorescence images. It is composed of three branches: Reflectance, Fluorescence and Integration. The branches are first trained individually and then fused together. Finally, the Deep Fusion Net is trained end-to-end for the classification of different coral genera and other non-coral classes. Experiments on the challenging Eliat Fluorescence Coral dataset show that the Deep Fusion net achieves superior classification accuracy compared to other methods.

Item Type: Conference Paper
Murdoch Affiliation: Information Technology, Mathematics and Statistics
URI: http://researchrepository.murdoch.edu.au/id/eprint/56783
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