Applied Intelligence

, Volume 18, Issue 2, pp 215–234

Change Detection in Overhead Imagery Using Neural Networks

  • Chris Clifton

DOI: 10.1023/A:1021942526896

Cite this article as:
Clifton, C. Applied Intelligence (2003) 18: 215. doi:10.1023/A:1021942526896


Identifying interesting changes from a sequence of overhead imagery—as opposed to clutter, lighting/seasonal changes, etc.—has been a problem for some time. Recent advances in data mining have greatly increased the size of datasets that can be attacked with pattern discovery methods. This paper presents a technique for using predictive modeling to identify unusual changes in images. Neural networks are trained to predict “before” and “after” pixel values for a sequence of images. These networks are then used to predict expected values for the same images used in training. Substantial differences between the expected and actual values represent an unusual change. Results are presented on both multispectral and panchromatic imagery.

change detectionoverhead imageryneural networks

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Chris Clifton
    • 1
  1. 1.Department of Computer SciencesPurdue UniversityWest Lafeyette