@ARTICLE\{IMM1998-01220,
    author       = "A. A. Nielsen and K. Conradsen and J. J. Simpson",
    title        = "Multivariate Alteration Detection (MAD) and {MAF} Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies",
    year         = "1998",
    pages        = "1-19",
    journal      = "Remote Sensing of Environment",
    volume       = "64",
    editor       = "",
    number       = "1",
    publisher    = "Elsevier",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/1220-full.html",
    abstract     = "This article introduces the multivariate alteration detection (MAD) transformation which is based on the established canonical correlations analysis. It also proposes using postprocessing of the change detected by the {MAD} variates using maximum autocorrelation factor (MAF) analysis. The {MAD} and the combined {MAF}/{MAD} transformations are invariant to linear scaling. Therefore, they are insensitive, for example, to differences in gain settings in a measuring device, or to linear radiometric and atmospheric correction schemes. Other multivariate change detection schemes described are principal component type analyses of simple difference images. Case studies with {AHVRR} and Landsat {MSS} data using simple linear stretching and masking of the change images show the usefulness of the new {MAD} and {MAF}/{MAD} change detection schemes. Ground truth observations confirm the detected changes. A simple simulation of a no-change situation shows the accuracy of the {MAD} and {MAF}/{MAD} transformations compared to principal components based methods.",
    isbn_issn    = "10.1016/S0034-4257(97)00162-4"
}