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. |
| Keywords | Airborne remote sensing, HyMap, feature selection |
| Type | Conference paper [With referee] |
| Conference | SPIE Europe Remote Sensing Conference 8180 |
| Year | 2011 Month September |
| Address | Prague, Czech Republic |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics, Geoinformatics |