Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation

Abdelwaheb Marzouki; Yves Delignon; Wojciech Pieczynski

Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation

Abdelwaheb Marzouki, Yves Delignon, Wojciech Pieczynski

École Nouvelle d'Ingénieurs en Communication

Research output: Contribution to conference › Paper › peer-review

Abstract

This work deals with the estimation of generalized mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be 'generalized' when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. We propose some methods of estimation of such mixtures based on Expectation-Maximization (EM), and Iterative Conditional Estimation (ICE, [6]) algorithms. The set of families of distributions is assumed to lie in Pearson's system.

Original language	English
Pages	706-708
Number of pages	3
Publication status	Published - 1 Jan 1996
Externally published	Yes
Event	Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, IGARSS'96. Part 1 (of 4) - Lincoln, NE, USA Duration: 28 May 1996 → 31 May 1996

Conference

Conference	Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, IGARSS'96. Part 1 (of 4)
City	Lincoln, NE, USA
Period	28/05/96 → 31/05/96

Cite this

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title = "Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation",

abstract = "This work deals with the estimation of generalized mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be 'generalized' when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. We propose some methods of estimation of such mixtures based on Expectation-Maximization (EM), and Iterative Conditional Estimation (ICE, [6]) algorithms. The set of families of distributions is assumed to lie in Pearson's system.",

author = "Abdelwaheb Marzouki and Yves Delignon and Wojciech Pieczynski",

year = "1996",

month = jan,

day = "1",

language = "English",

pages = "706--708",

note = "Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, IGARSS'96. Part 1 (of 4) ; Conference date: 28-05-1996 Through 31-05-1996",

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Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation. / Marzouki, Abdelwaheb; Delignon, Yves; Pieczynski, Wojciech.
1996. 706-708 Paper presented at Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, IGARSS'96. Part 1 (of 4), Lincoln, NE, USA.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation

AU - Marzouki, Abdelwaheb

AU - Delignon, Yves

AU - Pieczynski, Wojciech

PY - 1996/1/1

Y1 - 1996/1/1

N2 - This work deals with the estimation of generalized mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be 'generalized' when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. We propose some methods of estimation of such mixtures based on Expectation-Maximization (EM), and Iterative Conditional Estimation (ICE, [6]) algorithms. The set of families of distributions is assumed to lie in Pearson's system.

AB - This work deals with the estimation of generalized mixtures with applications to unsupervised statistical multisensor image segmentation. A mixture is said to be 'generalized' when the exact nature of the noise components is not known; one assumes, however, that each belongs to a finite known set of families of distributions. We propose some methods of estimation of such mixtures based on Expectation-Maximization (EM), and Iterative Conditional Estimation (ICE, [6]) algorithms. The set of families of distributions is assumed to lie in Pearson's system.

M3 - Paper

AN - SCOPUS:0029705717

SP - 706

EP - 708

T2 - Proceedings of the 1996 International Geoscience and Remote Sensing Symposium, IGARSS'96. Part 1 (of 4)

Y2 - 28 May 1996 through 31 May 1996

ER -

Unsupervised statistical segmentation of multispectral SAR images using generalized mixture estimation

Abstract

Conference

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