@CONFERENCE\{IMM2008-05667,
    author       = "A. A. Nielsen and M. J. Canty",
    title        = "Kernel principal component analysis for change detection",
    year         = "2008",
    month        = "sep",
    booktitle    = "{SPIE} Europe Remote Sensing Conference",
    volume       = "7109",
    series       = "",
    editor       = "Lorenzo Bruzzone, Claudia Notarnicola, Francesco Posa",
    publisher    = "SPIE",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/5667-full.html",
    abstract     = "Principal component analysis (PCA) is often used to detect change over time in remotely sensed images. A commonly used technique consists of finding the projections along the two eigenvectors for data consisting of two variables which represent the same spectral band covering the same geographical region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of {PCA} is used to carry out the analysis. Unlike ordinary {PCA,} kernel {PCA} with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially."
}