| Title | Segmentation of textured polarimetric SAR scenes by likelihood approximation |
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| Author | Beaulieu, J -M; Touzi, R |
| Source | IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers) vol. 42, no. 10, 2004 p. 2063-2072, https://doi.org/10.1109/TGRS.2004.835302 |
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| Year | 2004 |
| Alt Series | Earth Sciences Sector, Contribution Series 2005013 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Subjects | remote sensing; statistical methods; models; textures |
| Illustrations | graphs; radar image; digital images |
| Released | 2004 10 01 |
| Abstract | A hierarchical stepwise optimization process is developed for polarimetric synthetic aperture radar image segmentation. We show that image segmentation can be viewed as a likelihood approximation
problem. The likelihood segment merging criteria are derived using the multivariate complex Gaussian, the Wishart distribution, and the K-distribution. In the presence of spatial texture, the Gaussian-Wishart segmentation is not appropriate. The
K-distribution segmentation is more effective in textured forested areas. The validity of the product model is also assessed, and a field-adaptable segmentation strategy combining different criteria is examined. |
| GEOSCAN ID | 220473 |
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