Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions

Dalila Benboudjema; Florence Tupin; Wojciech Pieczynski; Marc Sigelle; Jean Marie Nicolas

doi:10.1109/IGARSS.2007.4423694

Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions

Dalila Benboudjema
, Florence Tupin
, Wojciech Pieczynski
, Marc Sigelle
, Jean Marie Nicolas

CNRS LTCI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3891-3894
Number of pages	4
ISBN (Print)	1424412129, 9781424412129
DOIs	https://doi.org/10.1109/IGARSS.2007.4423694
Publication status	Published - 1 Jan 2007
Event	2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 - Barcelona, Spain Duration: 23 Jun 2007 → 28 Jun 2007

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007
Country/Territory	Spain
City	Barcelona
Period	23/06/07 → 28/06/07

Keywords

Fisher distributions
Nonstatioanry triplet Markov field
Parameters estimation
Synthetic aperture radar (SAR) images
Unsupervised segmentation

Access to Document

10.1109/IGARSS.2007.4423694

Cite this

Benboudjema, D., Tupin, F., Pieczynski, W., Sigelle, M., & Nicolas, J. M. (2007). Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions. In 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 (pp. 3891-3894). Article 4423694 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2007.4423694

Benboudjema, Dalila ; Tupin, Florence ; Pieczynski, Wojciech et al. / Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions. 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007. Institute of Electrical and Electronics Engineers Inc., 2007. pp. 3891-3894 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{6bc264c606814604b436a86aef65650f,

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.",

keywords = "Fisher distributions, Nonstatioanry triplet Markov field, Parameters estimation, Synthetic aperture radar (SAR) images, Unsupervised segmentation",

author = "Dalila Benboudjema and Florence Tupin and Wojciech Pieczynski and Marc Sigelle and Nicolas, \{Jean Marie\}",

year = "2007",

month = jan,

day = "1",

doi = "10.1109/IGARSS.2007.4423694",

language = "English",

isbn = "1424412129",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3891--3894",

booktitle = "2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007",

note = "2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007 ; Conference date: 23-06-2007 Through 28-06-2007",

}

Benboudjema, D, Tupin, F , Pieczynski, W, Sigelle, M & Nicolas, JM 2007, Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions. in 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007., 4423694, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 3891-3894, 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, Barcelona, Spain, 23/06/07. https://doi.org/10.1109/IGARSS.2007.4423694

Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions. / Benboudjema, Dalila; Tupin, Florence ; Pieczynski, Wojciech et al.
2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007. Institute of Electrical and Electronics Engineers Inc., 2007. p. 3891-3894 4423694 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions

AU - Benboudjema, Dalila

AU - Tupin, Florence

AU - Pieczynski, Wojciech

AU - Sigelle, Marc

AU - Nicolas, Jean Marie

PY - 2007/1/1

Y1 - 2007/1/1

N2 - 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.

AB - 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.

KW - Fisher distributions

KW - Nonstatioanry triplet Markov field

KW - Parameters estimation

KW - Synthetic aperture radar (SAR) images

KW - Unsupervised segmentation

UR - https://www.scopus.com/pages/publications/79952038090

U2 - 10.1109/IGARSS.2007.4423694

DO - 10.1109/IGARSS.2007.4423694

M3 - Conference contribution

AN - SCOPUS:79952038090

SN - 1424412129

SN - 9781424412129

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 3891

EP - 3894

BT - 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007

Y2 - 23 June 2007 through 28 June 2007

ER -

Benboudjema D, Tupin F , Pieczynski W, Sigelle M, Nicolas JM. Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions. In 2007 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007. Institute of Electrical and Electronics Engineers Inc. 2007. p. 3891-3894. 4423694. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2007.4423694

Unsupervised segmentation of SAR images using Triplet Markov fields and Fisher noise distributions

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this