Change detection in multi-temporal dual polarization Sentinel-1 data

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution with an associated p-value and a factorization of this test statistic, change analysis in a time series of 19 multilook, dual polarization Sentinel-1 SAR data in the covariance matrix representation (with diagonal elements only) is carried out. The omnibus test statistic and its factorization detect if and when change occurs.
Original languageEnglish
Title of host publicationProceedings of Geoscience and Remote Sensing Symposium
Number of pages8
PublisherIEEE
Publication date2017
Pages3901-3908
ISBN (Print)9781509049516
DOIs
Publication statusPublished - 2017
Event2017 IEEE International Geoscience and Remote Sensing Symposium - Fort Worth, United States
Duration: 23 Jul 201628 Jun 2017

Conference

Conference2017 IEEE International Geoscience and Remote Sensing Symposium
CountryUnited States
CityFort Worth
Period23/07/201628/06/2017
SeriesIEEE International Geoscience and Remote Sensing Symposium Proceedings
ISSN2153-6996

Keywords

  • Synthetic aperture radar
  • Aircraft
  • Covariance matrices
  • Airports
  • Earth
  • Google
  • Histograms

Cite this

Nielsen, A. A., Canty, M. J., Skriver, H., & Conradsen, K. (2017). Change detection in multi-temporal dual polarization Sentinel-1 data. In Proceedings of Geoscience and Remote Sensing Symposium (pp. 3901-3908). IEEE. IEEE International Geoscience and Remote Sensing Symposium Proceedings https://doi.org/10.1109/IGARSS.2017.8127854
Nielsen, Allan Aasbjerg ; Canty, Morton J. ; Skriver, Henning ; Conradsen, Knut. / Change detection in multi-temporal dual polarization Sentinel-1 data. Proceedings of Geoscience and Remote Sensing Symposium. IEEE, 2017. pp. 3901-3908 (IEEE International Geoscience and Remote Sensing Symposium Proceedings).
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abstract = "Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution with an associated p-value and a factorization of this test statistic, change analysis in a time series of 19 multilook, dual polarization Sentinel-1 SAR data in the covariance matrix representation (with diagonal elements only) is carried out. The omnibus test statistic and its factorization detect if and when change occurs.",
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Nielsen, AA, Canty, MJ, Skriver, H & Conradsen, K 2017, Change detection in multi-temporal dual polarization Sentinel-1 data. in Proceedings of Geoscience and Remote Sensing Symposium. IEEE, IEEE International Geoscience and Remote Sensing Symposium Proceedings, pp. 3901-3908, 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, United States, 23/07/2016. https://doi.org/10.1109/IGARSS.2017.8127854

Change detection in multi-temporal dual polarization Sentinel-1 data. / Nielsen, Allan Aasbjerg; Canty, Morton J.; Skriver, Henning; Conradsen, Knut.

Proceedings of Geoscience and Remote Sensing Symposium. IEEE, 2017. p. 3901-3908 (IEEE International Geoscience and Remote Sensing Symposium Proceedings).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AB - Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution with an associated p-value and a factorization of this test statistic, change analysis in a time series of 19 multilook, dual polarization Sentinel-1 SAR data in the covariance matrix representation (with diagonal elements only) is carried out. The omnibus test statistic and its factorization detect if and when change occurs.

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KW - Aircraft

KW - Covariance matrices

KW - Airports

KW - Earth

KW - Google

KW - Histograms

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Nielsen AA, Canty MJ, Skriver H, Conradsen K. Change detection in multi-temporal dual polarization Sentinel-1 data. In Proceedings of Geoscience and Remote Sensing Symposium. IEEE. 2017. p. 3901-3908. (IEEE International Geoscience and Remote Sensing Symposium Proceedings). https://doi.org/10.1109/IGARSS.2017.8127854