A Neutral-Network-Fusion Architecture for Automatic Extraction of Oceanographic Features from Satellite Remote Sensing Imagery

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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1999
Accession Number
ADA371859

Entities

People

  • Benoit Zerr
  • Farid Askari

Organizations

  • SACLANT ASW Research Centre

Tags

Communities of Interest

  • Sensors
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Character Recognition
  • Detection
  • Detectors
  • Feature Extraction
  • Image Processing
  • Information Systems
  • Machine Learning
  • Nato
  • Network Architecture
  • Neural Networks
  • Pattern Recognition
  • Regions
  • Remote Sensing
  • Satellite Imaging
  • Sensor Fusion
  • Two Dimensional

Readers

  • Artificial Intelligence
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space
  • Space - Space Objects