Urban area change detection based on generalized likelihood ratio test

Weiying Zhao; Sylvain Lobry; Henri Maitre; Jean Marie Nicolas; Florence Tupin

doi:10.1109/Multi-Temp.2017.8035245

Urban area change detection based on generalized likelihood ratio test

Weiying Zhao
, Sylvain Lobry
, Henri Maitre
, Jean Marie Nicolas
, Florence Tupin

Université Paris-Saclay

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

Abstract

Change detection methods often use denoised data because the original speckle noise has a strong influence on the detection results. The effect of using different data sources (different equivalent number of looks, original data, denoised data) and different threshold methods are studied based on four kinds of generalized likelihood ratio test approaches. NL-SAR [1] denoised data and the corresponding spatially varying equivalent number of looks are taken into account in the detection procedure. The bi-temporal experimental results on simulated data, realistic synthetic Sentinel-1 SAR data show the improvement of using equivalent number of looks of denoised data and corresponding adaptive thresholds for change detection in urban areas.

Original language	English
Title of host publication	2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538633274
DOIs	https://doi.org/10.1109/Multi-Temp.2017.8035245
Publication status	Published - 12 Sept 2017
Externally published	Yes
Event	9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017 - Bruges, Belgium Duration: 27 Jun 2017 → 29 Jun 2017

Publication series

Name	2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017

Conference

Conference	9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017
Country/Territory	Belgium
City	Bruges
Period	27/06/17 → 29/06/17

Access to Document

10.1109/Multi-Temp.2017.8035245

Cite this

Zhao, W., Lobry, S., Maitre, H., Nicolas, J. M., & Tupin, F. (2017). Urban area change detection based on generalized likelihood ratio test. In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017 Article 8035245 (2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/Multi-Temp.2017.8035245

Zhao, Weiying ; Lobry, Sylvain ; Maitre, Henri et al. / Urban area change detection based on generalized likelihood ratio test. 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017).

@inproceedings{385d2eeefff84aa2a59d3d2f3c943795,

title = "Urban area change detection based on generalized likelihood ratio test",

abstract = "Change detection methods often use denoised data because the original speckle noise has a strong influence on the detection results. The effect of using different data sources (different equivalent number of looks, original data, denoised data) and different threshold methods are studied based on four kinds of generalized likelihood ratio test approaches. NL-SAR [1] denoised data and the corresponding spatially varying equivalent number of looks are taken into account in the detection procedure. The bi-temporal experimental results on simulated data, realistic synthetic Sentinel-1 SAR data show the improvement of using equivalent number of looks of denoised data and corresponding adaptive thresholds for change detection in urban areas.",

author = "Weiying Zhao and Sylvain Lobry and Henri Maitre and Nicolas, \{Jean Marie\} and Florence Tupin",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017 ; Conference date: 27-06-2017 Through 29-06-2017",

year = "2017",

month = sep,

day = "12",

doi = "10.1109/Multi-Temp.2017.8035245",

language = "English",

series = "2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017",

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

booktitle = "2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017",

}

Zhao, W, Lobry, S, Maitre, H, Nicolas, JM & Tupin, F 2017, Urban area change detection based on generalized likelihood ratio test. in 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017., 8035245, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017, Institute of Electrical and Electronics Engineers Inc., 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017, Bruges, Belgium, 27/06/17. https://doi.org/10.1109/Multi-Temp.2017.8035245

Urban area change detection based on generalized likelihood ratio test. / Zhao, Weiying; Lobry, Sylvain; Maitre, Henri et al.
2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8035245 (2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017).

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

TY - GEN

T1 - Urban area change detection based on generalized likelihood ratio test

AU - Zhao, Weiying

AU - Lobry, Sylvain

AU - Maitre, Henri

AU - Nicolas, Jean Marie

AU - Tupin, Florence

PY - 2017/9/12

Y1 - 2017/9/12

N2 - Change detection methods often use denoised data because the original speckle noise has a strong influence on the detection results. The effect of using different data sources (different equivalent number of looks, original data, denoised data) and different threshold methods are studied based on four kinds of generalized likelihood ratio test approaches. NL-SAR [1] denoised data and the corresponding spatially varying equivalent number of looks are taken into account in the detection procedure. The bi-temporal experimental results on simulated data, realistic synthetic Sentinel-1 SAR data show the improvement of using equivalent number of looks of denoised data and corresponding adaptive thresholds for change detection in urban areas.

AB - Change detection methods often use denoised data because the original speckle noise has a strong influence on the detection results. The effect of using different data sources (different equivalent number of looks, original data, denoised data) and different threshold methods are studied based on four kinds of generalized likelihood ratio test approaches. NL-SAR [1] denoised data and the corresponding spatially varying equivalent number of looks are taken into account in the detection procedure. The bi-temporal experimental results on simulated data, realistic synthetic Sentinel-1 SAR data show the improvement of using equivalent number of looks of denoised data and corresponding adaptive thresholds for change detection in urban areas.

UR - https://www.scopus.com/pages/publications/85032362951

U2 - 10.1109/Multi-Temp.2017.8035245

DO - 10.1109/Multi-Temp.2017.8035245

M3 - Conference contribution

AN - SCOPUS:85032362951

T3 - 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017

BT - 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017

Y2 - 27 June 2017 through 29 June 2017

ER -

Zhao W, Lobry S, Maitre H, Nicolas JM, Tupin F. Urban area change detection based on generalized likelihood ratio test. In 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8035245. (2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images, MultiTemp 2017). doi: 10.1109/Multi-Temp.2017.8035245

Urban area change detection based on generalized likelihood ratio test

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