Canonical Analysis of Sentinel-1 Radar and Sentinel-2 Optical Data |
Allan Aasbjerg Nielsen, Rasmus Larsen
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| Abstract | This paper gives results from joint analyses of dual polarimety synthetic aperture radar data from the Sentinel-1 mission and optical data from the Sentinel-2 mission. The analyses are carried out by means of traditional canonical correlation analysis (CCA) and canonical information analysis (CIA). Where CCA is based on maximising correlation between linear combinations of the two data sets, CIA maximises mutual information between the two. CIA is a conceptually more pleasing method for the analysis of data with very different modalities such as radar and optical data. Although a little inconclusive as far as the change detection aspect is concerned, results show that CIA analysis gives conspicuously less noisy appearing images of canonical variates (CVs) than CCA. Also, the 2D histogram of the CIA based leading CVs clearly reveals much more structure than the CCA based one. This gives promise for potentially better change detection results with CIA than can be obtained by means of CCA. |
| Keywords | canonical correlation analysis, canonical information analysis |
| Type | Conference paper [With referee] |
| Conference | Scandinavian Conference on Image Analysis (SCIA) |
| Year | 2017 Month June Vol. 10270 pp. 147-158 |
| Publisher | Springer LNCS |
| ISBN / ISSN | DOI: 10.1007/978-3-319-59129-2 |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |