Book chapter · Conference paper
Canonical analysis of sentinel-1 radar and sentinel-2 optical data
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 mutual information based leading CVs clearly reveals much more structure than the correlation based one. This gives promise for potentially better change detection results with CIA than can be obtained by means of CCA.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 147-158 |
| Proceedings: | 20th Scandinavian Conference on Image Analysis |
| Series: | Lecture Notes in Computer Science |
| Journal subtitle: | 20th Scandinavian Conference, Scia 2017, Tromsø, Norway, June 12–14, 2017, Proceedings, Part II |
| ISBN: | 3319591282 , 3319591290 , 9783319591285 and 9783319591292 |
| ISSN: | 16113349 and 03029743 |
| Types: | Book chapter and Conference paper |
| DOI: | 10.1007/978-3-319-59129-2_13 |
| ORCIDs: | Nielsen, Allan Aasbjerg and Larsen, Rasmus |
2-D histograms Analysis of data Canonical analysis Canonical correlation analysis Canonical information analysis Change detection Computer Science (all) Correlation methods Image analysis Information analysis Joint analysis Linear combinations Mutual informations Radar Synthetic aperture radar Theoretical Computer Science
