基于混合Wishart分布MRF分割的极化SAR图像海岸线检测

doi:10.3969/j.issn.1001-506X.2020.03.009

Abstract

Abstract:

The probability distribution of the sea or land is difficult to be modeled by a single Wishart distribution when segmenting the sea and land with markov random field (MRF) segmentation method in polarimetric synthetic aperture radar (SAR) imagery, a novel coastline detection method with MRF segmentation based on mixture Wishart distribution is proposed in this article. An initial sea-land segmentation result is obtained by thresholding segmentation of the edge energy map of the Span of polarimetric SAR image with the Ostu method at first. Then accurate sea-land segmentation is carried out by using the two-region MRF segmentation model, in which the distribution of the sea and land are modeled by a mixture of Wishart distribution. Finally, coastlines are extracted by tracing the boundary of the sea-land segmentation result after merging different water regions to get the final segmentation result. Polarimetric data acquired by TerraSAR over Sanfranciso region and RADARSAT2 over Singapore region are used for testing. Experiment results show that the proposed method is more accurate and robust compared with the MRF segmentation based on a single Wishart distribution.

Key words: synthetic aperture radar (SAR), coastline detection, Markov random field (MRF), mixture Wishart distribution

CLC Number:

TN957.52

Chun LIU, Junliang BAO, Jian YANG, Wenting MA, Jing WANG. Coastline detection in polarimetric SAR images using Markov random field segmentation based on mixture Wishart distribution[J]. Systems Engineering and Electronics, 2020, 42(3): 568-574.

Figures/Tables 6

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References 25

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Coastline detection in polarimetric SAR images using Markov random field segmentation based on mixture Wishart distribution

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