Polarimetric SAR image object segmentation via level set with stationary global minimum

Authors:
Yongmin Shuai;Hong Sun;Wen Yang
Affiliations:
Signal Processing Laboratory, Department of Communication Engineering, School of Electronic Information, Wuhan University, Wuhan, China;Signal Processing Laboratory, Department of Communication Engineering, School of Electronic Information, Wuhan University, Wuhan, China;Signal Processing Laboratory, Department of Communication Engineering, School of Electronic Information, Wuhan University, Wuhan, China
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on advances in multidimensional synthetic aperture radar signal processing
Year:
2010

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Abstract

We present a level set-based method for object segmentation in polarimetric synthetic aperture radar (PolSAR) images. In our method, a modified energy functional via active contour model is proposed based on complex Gaussian/Wishart distribution model for both single-look and multilook PolSAR images. The modified functional has two interesting properties: (1) the curve evolution does not enter into local minimum; (2) the level set function has a unique stationary convergence state. With these properties, the desired object can be segmented more accurately. Besides, the modified functional allows us to set an effective automatic termination criterion and makes the algorithm more practical. The experimental results on synthetic and real PolSAR images demonstrate the effectiveness of our method.