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Image change detection algorithms: a systematic survey.

Richard J Radke1, Srinivas Andra, Omar Al-Kofahi

  • 1Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. rjradke@ecse.rpi.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 15, 2005
PubMed
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This survey classifies modern change detection algorithms, crucial for applications like remote sensing and medical imaging. It guides researchers by organizing diverse methods into common categories for better algorithm design.

Area of Science:

  • Computer Science
  • Image Processing
  • Remote Sensing

Background:

  • Change detection in image sequences is vital across remote sensing, surveillance, medical imaging, and infrastructure monitoring.
  • Existing algorithms employ diverse processing steps and decision rules, making systematic classification challenging.

Purpose of the Study:

  • To systematically survey and classify modern change detection algorithms.
  • To provide guidance for algorithm designers by organizing methods into common categories.

Main Methods:

  • The survey reviews common processing steps: significance testing, predictive models, shading models, and background modeling.
  • It also covers preprocessing, change mask consistency enforcement, and performance evaluation principles.

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Main Results:

  • Algorithms are classified into a relatively small number of categories based on their core decision rules.
  • Key processing steps and evaluation principles are systematically presented.

Conclusions:

  • The proposed classification offers a structured overview of change detection techniques.
  • This framework aims to assist researchers and practitioners in selecting and developing effective change detection algorithms.