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Proceedings Paper

Towards spatial localisation of harmful algal blooms; statistics-based spatial anomaly detection
Author(s): J. D. Shutler; M. G Grant; P. I. Miller
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Paper Abstract

Harmful algal blooms are believed to be increasing in occurrence and their toxins can be concentrated by filter-feeding shellfish and cause amnesia or paralysis when ingested. As a result fisheries and beaches in the vicinity of blooms may need to be closed and the local population informed. For this avoidance planning timely information on the existence of a bloom, its species and an accurate map of its extent would be prudent. Current research to detect these blooms from space has mainly concentrated on spectral approaches towards determining species. We present a novel statistics-based background-subtraction technique that produces improved descriptions of an anomaly's extent from remotely-sensed ocean colour data. This is achieved by extracting bulk information from a background model; this is complemented by a computer vision ramp filtering technique to specifically detect the perimeter of the anomaly. The complete extraction technique uses temporal-variance estimates which control the subtraction of the scene of interest from the time-weighted background estimate, producing confidence maps of anomaly extent. Through the variance estimates the method learns the associated noise present in the data sequence, providing robustness, and allowing generic application. Further, the use of the median for the background model reduces the effects of anomalies that appear within the time sequence used to generate it, allowing seasonal variations in the background levels to be closely followed. To illustrate the detection algorithm's application, it has been applied to two spectrally different oceanic regions.

Paper Details

Date Published: 19 October 2005
PDF: 11 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820P (19 October 2005); doi: 10.1117/12.627684
Show Author Affiliations
J. D. Shutler, Plymouth Marine Lab. (United Kingdom)
M. G Grant, Univ. of Southampton (United Kingdom)
P. I. Miller, Plymouth Marine Lab. (United Kingdom)


Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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