Learning features from georeferenced seafloor imagery with location guided autoencoders.
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Although modern machine learning has the potential to greatly speed up the interpretation of imagery, the varied nature of the seabed and limited availability of expert annotations form barriers to its widespread use in seafloor mapping applications. This motivates research into unsupervised methods that function without large databases of human annotations. This paper develops an unsupervised feature learning method for georeferenced seafloor visual imagery that considers patterns both within the footprint of a single image frame and broader scale spatial characteristics. Features within images are learnt using an autoencoder developed based on the AlexNet deep convolutional neural network. Features larger than each image frame are learnt using a novel loss function that regularises autoencoder training using the Kullback–Leibler divergence function to loosely assume that images captured within a close distance of each other look more similar than those that are far away. The method i.....
JournalJournal of Field Robotics
Sustainable Development Goals (SDG)14.A
Maturity LevelTRL 7 System prototyping demonstration in an operational environment (ground or space)
Best Practice TypeStandard Operating Procedure
Spatial CoverageNorthwest Pacific Ocean
CitationYamada, T.; Prügel‐Bennett, A.; Thornton, B. (2020) Learning features from georeferenced seafloor imagery with location guided autoencoders. Journal of Field Robotics, 38, pp.52– 67. DOI: https://doi.org/10.1002/rob.21961
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