Lawen, JohannesJohannesLawenLawen, K.K.LawenSalman, G.G.SalmanSchuster, AssafAssafSchuster2022-03-142022-03-142022-03-04Water 14 (5): 810 (2022)http://hdl.handle.net/11420/11995The 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.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.en2073-444120225Multidisciplinary Digital Publishing Institutehttps://creativecommons.org/licenses/by/4.0/remote sensingmulti-bandSNNanomaly detectionshoreline recognitionTechnikIngenieurwissenschaftenJournal Article2022-03-1010.15480/882.424210.3390/w1405081010.15480/882.4242Journal Article