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Journal of Applied Remote Sensing

Approaching bathymetry estimation from high resolution multispectral satellite images using a neuro-fuzzy technique
Author(s): Linda Corucci; Andrea Masini; Marco Cococcioni
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Paper Abstract

This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [−18, −1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.

Paper Details

Date Published: 1 January 2011
PDF: 16 pages
J. Appl. Remote Sens. 5(1) 053515 doi: 10.1117/1.3569125
Published in: Journal of Applied Remote Sensing Volume 5, Issue 1
Show Author Affiliations
Linda Corucci, Univ. di Pisa (Italy)
Andrea Masini, Flyby S.r.l. (Italy)
Marco Cococcioni, Univ. di Pisa (Italy)

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