Unsupervised change detection in multispectral images based on independent component analysis

Michele Ceccarelli, A. Petrosino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. The paper proposes a data dependent change detection approach based on textural features extracted by the Independent Component Analysis (ICA) model. The properties of ICA allow to create energy features for computing multispectral and multitemporal difference images to be classified. Our experiments on remote sensing images show that the proposed method can efficiently and effectively classify temporal discontinuities corresponding to changed areas over the observed scenes.

Original languageEnglish
Title of host publicationIST 2006 - Proceedings of the 2006 IEEE International Workshop on Imagining Systems and Techniques
Pages54-59
Number of pages6
Volume2006
DOIs
Publication statusPublished - 22 Dec 2006
Externally publishedYes
EventIST 2006 - 2006 IEEE International Workshop on Imagining Systems and Techniques - Ninori, Italy
Duration: 29 Apr 200629 Apr 2006

Other

OtherIST 2006 - 2006 IEEE International Workshop on Imagining Systems and Techniques
CountryItaly
CityNinori
Period29/4/0629/4/06

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ceccarelli, M., & Petrosino, A. (2006). Unsupervised change detection in multispectral images based on independent component analysis. In IST 2006 - Proceedings of the 2006 IEEE International Workshop on Imagining Systems and Techniques (Vol. 2006, pp. 54-59). [1650775] https://doi.org/10.1109/IST.2006.1650775