Linear and kernel methods for multi- and hypervariate change detection |
Allan Aasbjerg Nielsen, Morton John Canty
|
| Abstract | The paper gives an overview of unsupervised, automatic change detection methods particularly the MAD method which is based on an iterated version of canonical correlation analysis with subsequent post-processing by means of linear and kernel versions of principal component analysis, maximum autocorrelation factor analysis, and minimum noise fraction analysis. |
| Type | Conference paper [With referee] |
| Conference | SPIE Europe Remote Sensing Conference 7830 |
| Year | 2010 Month September |
| Note | Invited contribution |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Geoinformatics |