Binary Programming Models of Spatial Pattern Recognition: Applications in Remote Sensing Image Analysis

Abstract

The major purpose of this investigation was to implement subregion allocation objectives using a network model base on an existing subregion allocation binary programming model (Benabdallah and Wright (B&W), 1990), the ultimate goal being the application of subregion allocation concepts towards the spatial analysis of satellite imagery. The multi-objective aspects of subregion allocation can be accomplished via a network formulation, a formulation vastly simpler in complexity than the binary programming models previously used. Without a network programming package that could maintain integral flows, however, deriving the solution was a tiresome task for the user. Nonetheless, several new concepts and advantages to using networks were discussed and demonstrated to the degree possible. Spatial analysis could benefit from applying a modified version of the B&W model to the pixels of satellite imagery in a way which takes advantage of the inherent contiguity of natural terrain subregions. Trending and forecasting of subregion changes, tasks that rely on acquiring data in a consistent manner image after image, could benefit due to the fact that major spatial characteristics of subregions could be extracted, and minor spatial changes could be removed.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA243797

Entities

People

  • Thomas G. Reed

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Space

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Change Detection
  • Computer Programming
  • Computer Programs
  • Computer Vision
  • Computers
  • Detection
  • Feature Extraction
  • Image Segmentation
  • Images
  • Information Systems
  • Operating Systems
  • Pattern Recognition
  • Recognition
  • Satellite Imaging
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Spacecraft Maneuvers