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Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

J. Lawen / K. Lawen / G. Salman / A. Schuster

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.

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Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

J. Lawen / K. Lawen / G. Salman / A. Schuster

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.

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

J. Lawen / K. Lawen / G. Salman / A. Schuster

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.

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Titel:

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

Beteiligte:

J. Lawen (Autor:in) / K. Lawen (Autor:in) / G. Salman (Autor:in) / A. Schuster (Autor:in)

Erschienen in:

Water, Vol 14, Iss 5, p 810 (2022)

Erscheinungsdatum:

2022

ISSN:

DOI:

https://doi.org/10.3390/w14050810

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Unbekannt

Schlagwörter:

remote sensing , multi-band , SNN , anomaly detection , shoreline recognition , Hydraulic engineering , TC1-978 , Water supply for domestic and industrial purposes , TD201-500

Metadata by DOAJ is licensed under CC BY-SA 1.0

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