Assessment of Multi-Sensor Neural Image Fusion and Fused Data Mining for Land Cover Classification

Abstract

Recent studies suggest that the combination of imagery from earth observation satellites with complementary spectral, spatial, and temporal information may provide improved land cover classification performance. This paper assesses the benefits of new biologically-based image fusion and fused data mining methods for improving discrimination between spectrally-similar land cover classes using multi-spectral, multi-sensor, and multitemporal imagery. For this investigation multi-season Landsat and Radarsat imagery of a forest region in central New York State was processed using opponent-band image fusion, multi-scale visual texture and contour enhancement, and the Fuzzy ARTMAP neural classifier. These methods are shown to enable identification of sub-categories of land cover and provide improved classification accuracy compared to traditional statistical methods.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2006
Accession Number
ADA521770

Entities

People

  • A. Waxman
  • D. Fay
  • M. Pugh

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Aerial Photographs
  • Algorithms
  • Classification
  • Coordinate Systems
  • Data Mining
  • Detection
  • Detectors
  • Differential Geometry
  • Geometry
  • High Resolution
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Synthetic Aperture Radar
  • Three Dimensional

Readers

  • Computer Vision.
  • Sensor Fusion and Tracking Systems.
  • Wetland-Land-Environmental Management.

Technology Areas

  • AI & ML
  • Space
  • Space - Space Objects