Automatic Extraction of Drainage Network from Digital Terrain Elevation Data (DTED).

Abstract

This report describes an approach to the automatic extraction of a drainage network from Digital Terrain Elevation Data (DTED). The extraction of drainage networks from topological data is one of the more important uses of such information. An extracted network may be used in many applications, such as determining drainage network metrics or selecting control points for image registration and mapping applications. Neural networks have been shown to possess generalization properties that make them well suited to this problem, which calls for a generalized solution. In this research, a biologically inspired network has been designed and applied to automatic extraction of drainage networks. Several DTED test files were used for testing of the developed model. The results indicate the success of neural networks in the extraction of drainage networks from DTED.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1996
Accession Number
ADA311189

Entities

People

  • Alan Fern
  • Curt Ruffing
  • Greg Hawkins
  • Mohamad T. Musavi

Organizations

  • University of Maine

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Automatic
  • Elevation
  • Extraction
  • Image Registration
  • Neural Networks

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Graph Algorithms and Convex Optimization.
  • Polar and Arctic Studies

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks