Conference paper
Sparse principal component analysis in hyperspectral change detection
Geodesy, National Space Institute, Technical University of Denmark1
National Space Institute, Technical University of Denmark2
Image Analysis and Computer Graphics, Department of Informatics and Mathematical Modeling, Technical University of Denmark3
Department of Informatics and Mathematical Modeling, Technical University of Denmark4
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.
| Language: | English |
|---|---|
| Publisher: | SPIE |
| Year: | 2011 |
| Pages: | 81800S-6 |
| Proceedings: | Image and Signal Processing for Remote Sensing XVII |
| ISSN: | 1996756x and 0277786x |
| Types: | Conference paper |
| DOI: | 10.1117/12.897434 |
| ORCIDs: | Nielsen, Allan Aasbjerg and Larsen, Rasmus |
