Multi-band bathymetry mapping with spiking neuron anomaly detection

The developed method extracts bathymetry distributions from multiple satellite image bands. The automated remote sensing function is sparsely coded and combines spiking neural net anomaly filtration, spline, and multi-band fittings. Survey data were used to identify an activation threshold, decay rate, spline fittings, and multi-band weighting factors. Errors were computed for remotely sensed Landsat satellite images. Multi-band fittings achieved an average error of 25.3 cm. This proved sufficiently accurate to automatically extract shorelines to eliminate land areas in bathymetry mapping.

Subjects

remote sensing

multi-band

SNN

anomaly detection

shoreline recognition

DDC Class

600: Technik

620: Ingenieurwissenschaften

Funding Organisations

European Union

More Funding Information

This research was funded by European Union’s Horizon 2020 Research and Innovation Program (H2020-BG-12-2016-2), grant number No. 727277—ODYSSEA (Towards an integrated Mediterranean Sea Observing System). The article reflects only authors’ view and that the Commission is not responsible for any use that may be made of the information it contains.

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https://creativecommons.org/licenses/by/4.0/

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water-14-00810-v2.pdf

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5.41 MB

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Multi-band bathymetry mapping with spiking neuron anomaly detection