Follow Us:

View Item 
  •   CMRE Open Library Home
  • CMRE Publications
  • Technical Reports
  • View Item
  •   CMRE Open Library Home
  • CMRE Publications
  • Technical Reports
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A neural-network-fusion architecture for automatic extraction of oceanographic features from satellite remote sensing imagery

Thumbnail
Abstract
This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of
 
evidence. Deterministic or idealized shapes are used to characterize surface signatures of oceanic and atmospheric fronts manifested in satellite remote sensing imagery. Raw satellite images are processed by a bank of radial basis function (RBF) neural networks trained on idealized shapes. The final classification results from the fusion of the outputs of the separate RBF. The fusion mechanism is based on DS evidential reasoning theory. The approach is initially tested for detecting different features on a single sensor and extended to classifying features observed by multiple sensors.
 

URI
http://hdl.handle.net/20.500.12489/541

Report Number
SR-306

Collections
  • Technical Reports

Date
1999/06

Author(s)
Askari, Farid
; 
Zerr, Benoit

Show full item record
SR-306-UU.pdf (3.622Mb)

Browse

All of CMRE Open LibraryCommunities & CollectionsBy Issue DateAuthor(s)TitlesSubjectsTypeThis CollectionBy Issue DateAuthor(s)TitlesSubjectsType

My Account

LoginRegister

  • Contact Us
  • Send Feedback
  • Employment
  • Procurement
  • Fact Sheets
  • News Feed
  • Conditions of Use
  • Publications Feed
  • Press Release
  • News Archive
  • STO (Science and Technology Organization)
  • Find us on Facebook
  • Follow us on Twitter
  • Watch us on Youtube
  • Webmail
 

 

© 2018 STO-CMRE
Powered by KnowledgeArc