Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing

Xin Su; Charles Alban Deledalle; Florence Tupin; Hong Sun

doi:10.1109/IGARSS.2014.6946705

Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing

Xin Su, Charles Alban Deledalle, Florence Tupin, Hong Sun

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

Abstract

This paper presents a change detection and classification method of Synthetic Aperture Radar (SAR) multi-temporal images. The change criterion based on a generalized likelihood ratio test is an extension of the likelihood ratio test, in which both the noisy data and the multi-temporal denoised data are used. The changes are detected by a thresholding and then classified into step, impulse and cycle changes according to their temporal behaviors. The results show the effective performance of the proposed method.

Original language	English
Title of host publication	International Geoscience and Remote Sensing Symposium (IGARSS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1433-1436
Number of pages	4
ISBN (Electronic)	9781479957750
DOIs	https://doi.org/10.1109/IGARSS.2014.6946705
Publication status	Published - 4 Nov 2014
Event	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada Duration: 13 Jul 2014 → 18 Jul 2014

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/Territory	Canada
City	Quebec City
Period	13/07/14 → 18/07/14

Keywords

Generalized likelihood ratio test
Multi-Temporal Synthetic Aperture Radar (SAR)
change classification
change detection

Access to Document

10.1109/IGARSS.2014.6946705

Cite this

Su, X., Deledalle, C. A., Tupin, F., & Sun, H. (2014). Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 1433-1436). Article 6946705 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2014.6946705

Su, Xin ; Deledalle, Charles Alban ; Tupin, Florence et al. / Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing. International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1433-1436 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{2ff28da745c24a8681b08fa935db9fb5,

title = "Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing",

abstract = "This paper presents a change detection and classification method of Synthetic Aperture Radar (SAR) multi-temporal images. The change criterion based on a generalized likelihood ratio test is an extension of the likelihood ratio test, in which both the noisy data and the multi-temporal denoised data are used. The changes are detected by a thresholding and then classified into step, impulse and cycle changes according to their temporal behaviors. The results show the effective performance of the proposed method.",

keywords = "Generalized likelihood ratio test, Multi-Temporal Synthetic Aperture Radar (SAR), change classification, change detection",

author = "Xin Su and Deledalle, \{Charles Alban\} and Florence Tupin and Hong Sun",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 ; Conference date: 13-07-2014 Through 18-07-2014",

year = "2014",

month = nov,

day = "4",

doi = "10.1109/IGARSS.2014.6946705",

language = "English",

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

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

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Su, X, Deledalle, CA, Tupin, F & Sun, H 2014, Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing. in International Geoscience and Remote Sensing Symposium (IGARSS)., 6946705, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 1433-1436, Joint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014, Quebec City, Canada, 13/07/14. https://doi.org/10.1109/IGARSS.2014.6946705

Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing. / Su, Xin; Deledalle, Charles Alban; Tupin, Florence et al.
International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc., 2014. p. 1433-1436 6946705 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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

TY - GEN

T1 - Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing

AU - Su, Xin

AU - Deledalle, Charles Alban

AU - Tupin, Florence

AU - Sun, Hong

PY - 2014/11/4

Y1 - 2014/11/4

N2 - This paper presents a change detection and classification method of Synthetic Aperture Radar (SAR) multi-temporal images. The change criterion based on a generalized likelihood ratio test is an extension of the likelihood ratio test, in which both the noisy data and the multi-temporal denoised data are used. The changes are detected by a thresholding and then classified into step, impulse and cycle changes according to their temporal behaviors. The results show the effective performance of the proposed method.

AB - This paper presents a change detection and classification method of Synthetic Aperture Radar (SAR) multi-temporal images. The change criterion based on a generalized likelihood ratio test is an extension of the likelihood ratio test, in which both the noisy data and the multi-temporal denoised data are used. The changes are detected by a thresholding and then classified into step, impulse and cycle changes according to their temporal behaviors. The results show the effective performance of the proposed method.

KW - Generalized likelihood ratio test

KW - Multi-Temporal Synthetic Aperture Radar (SAR)

KW - change classification

KW - change detection

U2 - 10.1109/IGARSS.2014.6946705

DO - 10.1109/IGARSS.2014.6946705

M3 - Conference contribution

AN - SCOPUS:84911385005

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 1433

EP - 1436

BT - International Geoscience and Remote Sensing Symposium (IGARSS)

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 July 2014 through 18 July 2014

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Su X, Deledalle CA, Tupin F, Sun H. Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing. In International Geoscience and Remote Sensing Symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc. 2014. p. 1433-1436. 6946705. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2014.6946705

Change detection and classification of multi-temporal SAR series based on generalized likelihood ratio comparing-and-recognizing

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