Change detection in bi-temporal data by canonical information analysis

Allan Aasbjerg Nielsen, Jacob Schack Vestergaard

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Canonical correlation analysis (CCA) is an established multivariate statistical method for finding similarities between linear combinations of (normally two) sets of multivariate observations. In this contribution we replace (linear) correlation as the measure of association between the linear combinations with the information theoretical measure mutual information (MI). We term this type of analysis canonical information analysis (CIA). MI allows for the actual joint distribution of the variables involved and not just second order statistics. Where CCA is ideal for Gaussian data, CIA facilitates analysis of variables with different genesis and therefore different statistical distributions. As a proof of concept we give a toy example. We also give an example with DLR 3K camera data from two time points covering a motor way.
Original languageEnglish
Title of host publicationProceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp 2015)
Number of pages4
PublisherIEEE
Publication date2015
ISBN (Print)978-1-4673-7119-3
DOIs
Publication statusPublished - 2015
Event8th International Workshop on the Analysis of Multitemporal Remote Sensing Images - Annecy, France
Duration: 22 Jul 201524 Jul 2015
Conference number: 8

Workshop

Workshop8th International Workshop on the Analysis of Multitemporal Remote Sensing Images
Number8
CountryFrance
CityAnnecy
Period22/07/201524/07/2015

Cite this

Nielsen, A. A., & Vestergaard, J. S. (2015). Change detection in bi-temporal data by canonical information analysis. In Proceedings of the 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp 2015) IEEE. https://doi.org/10.1109/Multi-Temp.2015.7245779