Skip to main content

The use of Neural Networks for the estimation of oceanic constituents based on the MERIS instrument

Buy Article:

$55.00 plus tax (Refund Policy)

Artificial Neural Networks (NNs) are used in estimations of oceanic constituents from simulated data for the Mechron Resolution Imaging Spectrometer (MERIS) instrument system for Case II water applications. The simulation includes the effects of oceanic substances such as algal related chlorophyll, non-chlorophyllous suspended matter and DOM (dissolved organic matter). It is shown here that NNs can be used to estimate oceanic constituents based on simulated data which include the effects of realistic noise and variability models. The advantage of NNs is that they not only achieve higher retrieval accuracy than more traditional techniques such as band ratio algorithms, but they also allow the inclusion of usually superfluous or unused information, such as geometric parameters and atmospheric visibility.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Publication date: 1999-06-15

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more