NDLI: Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters

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Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Content Provider	Springer Nature Link
Author	Nagamani, P. V. Chauhan, Prakash Sanwlani, Nivedita Ali, M. M.
Copyright Year	2011
Abstract	Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.
Starting Page	137
Ending Page	143
Page Count	7
File Format	PDF
ISSN	0255660X
Journal	Journal of the Indian Society of Remote Sensing
Volume Number	40
Issue Number	1
e-ISSN	09743006
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-07-01
Publisher Place	India
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Benthic substrate Neural networks Inversion Sea grass Coral sand Earth Sciences Remote Sensing/Photogrammetry
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Geography, Planning and Development

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