Publication: Research › Conference article – Annual report year: 2011
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.
|Journal||Proceedings of the SPIE - The International Society for Optical Engineering|
|Conference||Image and Signal Processing for Remote Sensing XVII|
|Period||01-01-11 → …|
|Citations||Web of Science® Times Cited: 0|
- Feature selection, HyMap, Airborne remote sensing
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