@CONFERENCE\{IMM2011-06033,
    author       = "A. A. Nielsen and R. Larsen and J. S. Vestergaard",
    title        = "Sparse principal component analysis in hyperspectral change detection",
    year         = "2011",
    month        = "sep",
    keywords     = "Airborne remote sensing, HyMap, feature selection",
    booktitle    = "{SPIE} Europe Remote Sensing Conference 8180",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "Prague, Czech Republic",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6033-full.html",
    abstract     = "This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bi-temporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse {PCA} the resulting change scores appear visually very similar although the loadings are very different from their usual non-sparse counterparts. The choice of three wavelength regions as being most important for change detection demonstrates the feature selection capability of sparse {PCA}."
}