Name
Title | ||
|---|---|---|
Unsupervised Segmentation Of Sar Images Using Triplet Markov Fields And Fisher Noise Distributions |
Abstract | ||
|---|---|---|
This paper deals with SAR data segmentation in an unsupervised way. The model we propose is a combination of the nonstationary triplet Markov field recently introduced and the Fisher distributions. The first one allows modeling the different stationarities present in a given image. The second one has the advantage that is well adapted to this kind of data. We present an original technique based on Iterative Conditional Estimation method, to estimate the parameters of the model we propose. Application examples on simulated data and real SAR images are presented as well. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4423694 | IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET |
Keywords | Field | DocType |
nonstatioanry triplet Markov field, Fisher distributions, Synthetic aperture radar (SAR) images, parameters estimation, unsupervised segmentation | Computer vision,Data segment,Markov process,Pattern recognition,Synthetic aperture radar,Computer science,Segmentation,Remote sensing,Markov chain,Image segmentation,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2153-6996 | 4 | 0.53 |
References | Authors | |
5 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dalila Benboudjema | 1 | 96 | 5.52 |
| Florence Tupin | 2 | 1322 | 109.27 |
| W Pieczynski | 3 | 279 | 31.04 |
| Marc Sigelle | 4 | 316 | 34.12 |
| Jean-Marie Nicolas | 5 | 677 | 284.74 |
| Département CITI | 6 | 4 | 0.53 |
| CNRS UMR | 7 | 7 | 2.11 |