Coherence estimation from complex coherence map using second kind statistics

Abdelfattah, Riadh; Nicolas, J. M.

doi:10.1109/icip.2005.1530033

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The coherence map is generated according to (Abdelfattah, 2005). The estimation of coherence magnitude is made by spatially averaging the amplitude as well as the phase of the M pixel values from the complex map.…”

Section: Coherence Generationmentioning

confidence: 99%

Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

Zhang¹,

Fu²,

Sun³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The interferometric coherence map derived from the cross-correlation of two complex registered synthetic aperture radar (SAR) images is the reflection of imaged targets. In many applications, it can act as an independent information source, or give additional information complementary to the intensity image. Specially, the statistical properties of the coherence are of great importance in land cover classification, segmentation and change detection. However, compared to the amount of work on the statistical characters of SAR intensity, there are quite fewer researches on interferometric SAR (InSAR) coherence statistics. And to our knowledge, all of the existing work that focuses on InSAR coherence statistics, models the coherence with Gaussian distribution with no discrimination on data resolutions or scene types. But the properties of coherence may be different for different data resolutions and scene types. In this paper, we investigate on the coherence statistics for high resolution data over urban areas, by making a comparison of the accuracy of several typical statistical models. Four typical land classes including buildings, trees, shadow and roads are selected as the representatives of urban areas. Firstly, several regions are selected from the coherence map manually and labelled with their corresponding classes respectively. Then we try to model the statistics of the pixel coherence for each type of region, with different models including Gaussian, Rayleigh, Weibull, Beta and Nakagami. Finally, we evaluate the model accuracy for each type of region. The experiments on TanDEM-X data show that the Beta model has a better performance than other distributions.

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Section: Coherence Generationmentioning

confidence: 99%

Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

Zhang¹,

Fu²,

Sun³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The used coherence estimation approach is developed and published by the authors in [1]. It consists on the estimation of the coherence magnitude from a complex (amplitude and phase) coherence map computed between the SAR images using the second kind statistics defined with the log-moments.…”

Section: The Second Kind Statistics Coherence Estimatormentioning

confidence: 99%

“…i=l (1) where y is the multi-look complex coherence, i is the sample number and M is the number of integrated multi-look coherence samples. The SAR processor), geometric (registration of the interferometric pair, the baseline) and temporal (time separation between acquisitions).…”

Section: The Second Kind Statistics Coherence Estimatormentioning

confidence: 99%

“…The InSAR coherence is computed by using both the amplitude and the phase from the complex SAR image pair. It gives an estimate of the noise content in the interferogram: the phase difference between the two co-registered complex images [1].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we are interested on the analysis of the InSAR coherence image changes from different land surface categories (lake, bare soil, urban, agriculture, mountains). The coherence estimation is achieved using the second kind statistics estimator [1] which gives the best estimation accuracy for medium and high coherence values. The analysis is based on the extraction of coherence signatures using local histogram on the coherence map.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interferometric Synthetic Aperture Radar Coherence Histogram Analysis for Land Cover Classification

Abdelfattah

Nicolas

2006 2nd International Conference on Information &Amp; Communication Technologies

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SAR interferometry has a large potential for topographic and geophysical parameters extraction algorithms. The interferometric coherence, derived from the cross-correlation of two complex coregistered SAR images, gives additional information complementary to information contained in the amplitude value of the backscatterirng coefficieint. However, the irnterferometrnc phase is conrsidered with less importance as it has no effective relationship with different land covers on the image.Applications of the irnterferometric SAR (InSAR) coherence for land use classification is discussed. We proposed an origirnal land cover classification methodology based on the InSAR coherence histogram analysts. It conrsists on characterizirng each land surface categories (lake, bare soil, urban ...) with a signature formed with a probability denrsity function (pdf) computed from the correspondirng InSAR coherence class. Both qualitative and quanrtitative analysis are presented and discussed.

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Analysis of the unbiased complex coherence estimation using varying ERS interferometric data

Abdelfattah¹,

Nicolas²

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05.

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Coherence estimation from complex coherence map using second kind statistics

Cited by 6 publications

References 12 publications

Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

Model Accuracy Comparison for High Resolution Insar Coherence Statistics Over Urban Areas

Interferometric Synthetic Aperture Radar Coherence Histogram Analysis for Land Cover Classification

Analysis of the unbiased complex coherence estimation using varying ERS interferometric data

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