A Constrained-Clustering Approach to the Analysis of Remote Sensing Data.

Abstract

One old and two new clustering methods were applied to the constrained-clustering problem of separating different agricultural fields based on multispectral remote sensing satellite data. (Constrained-clustering involves double clustering in multispectral measurement similarity and geographical location.) The results of applying the three methods are provided along with a discussion of their relative strengths and weaknesses and a detailed description of their algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1983
Accession Number
ADA139124

Entities

People

  • Caillin J. Ryan

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Satellites
  • Classification
  • Computer Programs
  • Data Analysis
  • Data Science
  • Factor Analysis
  • High Density
  • Information Science
  • Literature Surveys
  • Mathematics
  • Measurement
  • New York
  • Remote Sensing
  • Statistics
  • Surveys

Readers

  • Computer Vision.

Technology Areas

  • Space