A Hybrid Methodology for Detecting Cartographically Significant Features Using Landsat TM Imagery

Abstract

A general Change Detection (CD) methodology is investigated that involves a hybrid mix of image processing, spectral transformation, and statistical pattern recognition techniques. The Hybrid Methodology attempts to combine various forms of supporting and conflicting evidence for change into a resulting change map. The approach involves differencing registered multiband scene pairs that have undergone a spectral transformation, generating threshold masks, and applying a classifier to the masked multiband scene pairs.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1991
Accession Number
ADA240454

Entities

People

  • Robert S. Rand

Organizations

  • Geospatial Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Data Mining
  • Data Science
  • Factor Analysis
  • Image Processing
  • Information Processing
  • Information Science
  • Machine Learning
  • New York
  • Pattern Recognition
  • Physical Properties
  • Probability
  • Regression Analysis
  • Statistical Algorithms
  • Statistics
  • Supervised Machine Learning
  • Urban Areas

Readers

  • Computational Modeling and Simulation
  • Computer Vision.

Technology Areas

  • AI & ML