@CONFERENCE\{IMM2017-06963,
    author       = "A. A. Nielsen and R. Larsen",
    title        = "Canonical Analysis of Sentinel-1 Radar and Sentinel-2 Optical Data",
    year         = "2017",
    month        = "jun",
    keywords     = "canonical correlation analysis, canonical information analysis",
    pages        = "147-158",
    booktitle    = "Scandinavian Conference on Image Analysis (SCIA)",
    volume       = "10270",
    series       = "",
    editor       = "",
    publisher    = "Springer {LNCS}",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6963-full.html",
    abstract     = "This paper gives results from joint analyses of dual polarimety synthetic aperture radar data from the Sentinel-1 mission and optical data from the Sentinel-2 mission. The analyses are carried out by means of traditional canonical correlation analysis (CCA) and canonical information analysis (CIA). Where {CCA} is based on maximising correlation between linear combinations of the two data sets, {CIA} maximises mutual information between the two. {CIA} is a conceptually more pleasing method for the analysis of data with very different modalities such as radar and optical data. Although a little inconclusive as far as the change detection aspect is concerned, results show that {CIA} analysis gives conspicuously less noisy appearing images of canonical variates (CVs) than {CCA}. Also, the {2D} histogram of the {CIA} based leading CVs clearly reveals much more structure than the {CCA} based one. This gives promise for potentially better change detection results with {CIA} than can be obtained by means of {CCA}.",
    isbn_issn    = "DOI: 10.1007/978-3-319-59129-2"
}