Neural-Inspired Anomaly Detection

Verzi, Stephen Joseph; Vineyard, Craig Michael; Aimone, James B.

doi:10.1007/978-3-319-96661-8_21

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2022

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“…In pursuit of the exploitation of yet less explored spiking neurons, bathymetric anomaly detection appears as a suitable commencing increment, as the complexity is then limited to a scalar depth derived from a particular pixel. Spiking neural nets have, hence, heretofore found application in anomaly detection for time series [8] and image processing [9]. In this work, spiking neural nets are used to detect anomalies in bathymetric data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Band Bathymetry Mapping with Spiking Neuron Anomaly Detection

Lawen

Salman

et al. 2022

Water

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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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Section: Introductionmentioning

confidence: 99%