26 October 2011 Sparse principal component analysis in hyperspectral change detection
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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.
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Allan A. Nielsen, Allan A. Nielsen, Rasmus Larsen, Rasmus Larsen, Jacob S. Vestergaard, Jacob S. Vestergaard, "Sparse principal component analysis in hyperspectral change detection", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800S (26 October 2011); doi: 10.1117/12.897434; https://doi.org/10.1117/12.897434

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