Sparse principal component analysis in hyperspectral change detection

Allan Aasbjerg Nielsen, Rasmus Larsen, Jacob Schack Vestergaard

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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.
Original languageEnglish
JournalProceedings of the SPIE - The International Society for Optical Engineering
Volume8180
Pages (from-to)81800S
ISSN0277-786X
DOIs
Publication statusPublished - 2011
EventImage and Signal Processing for Remote Sensing XVII - USA, Prague
Duration: 1 Jan 2011 → …

Conference

ConferenceImage and Signal Processing for Remote Sensing XVII
CityUSA, Prague
Period01/01/2011 → …

Keywords

  • Feature selection
  • HyMap
  • Airborne remote sensing

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