Name
Title | ||
|---|---|---|
Mean-Shift And Hierarchical Clustering For Textured Polarimetric Sar Image Segmentation/Classification |
Abstract | ||
|---|---|---|
Image segmentation and unsupervised classification are difficult problems. We propose to combine both. A clustering process is applied over segment mean values. Only large segments are considered. The clustering is composed of a mean-shift step and a hierarchical clustering step. The hierarchical grouping is based upon a powerful segmentation technique previously developed [1]. The approach is applied on a 9-look polarimetric SAR image. Textured and non-textured image regions are considered. The K and Wishart distributions are used respectively. The unsupervised classification results can be very useful for image analysis and further supervised classification. The obtained region groups constitute an important simplification of the image. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IGARSS.2010.5653919 | 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM |
Keywords | Field | DocType |
Polarimetric SAR image, hierarchical segmentation, mean-shift, texture, classification, clustering | Scale-space segmentation,Computer science,Remote sensing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Contextual image classification,Cluster analysis,Hierarchical clustering,Computer vision,Canopy clustering algorithm,Pattern recognition,Image texture | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.43 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-Marie Beaulieu | 1 | 96 | 19.25 |
| Ridha Touzi | 2 | 214 | 25.43 |