Level set model for water region segmentation in synthetic aperture radar images

Wentao Lyu; Jiawei Ren; Xiaomin Bao; Qingjiang Shi

doi:10.1117/1.JRS.13.026510

4 June 2019 Level set model for water region segmentation in synthetic aperture radar images

Wentao Lyu, Jiawei Ren, Xiaomin Bao, Qingjiang Shi

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026510 (June 2019). https://doi.org/10.1117/1.JRS.13.026510

Abstract

A level set model is presented for water region segmentation in synthetic aperture radar (SAR) images. We formulate the segmentation problem within a global energy minimization framework. First, the background and foreground regions in SAR images are modeled as G⁰ distributions. They are then used to construct the energy functional for the desired regions. To avoid the local minimum problem, the energy functional is transferred into a strictly convex model that guarantees the existence of the global minimum. During the iterative process, a sinusoidal signed pressure force (SPF) function is applied to efficiently locate weak or blurred edges in the heterogeneous regions. Finally, a Gaussian convolution is used to equivalently substitute the Laplacian of the level set function in the evolution equation, which omits the reinitialization at each iteration. Since based on the stationary global minimum, the presented model can accurately detect inside edges, regardless of the position and shape of the initial contour. Furthermore, because the SPF function can enhance the acquisition ability to the target contour, the internal and external motions of the curve can be accelerated. Thus, the convergence speed of the curve can be improved significantly. The experimental results based on the simulated and real SAR data demonstrate the effectiveness of our method.

Citation Download Citation

Wentao Lyu, Jiawei Ren, Xiaomin Bao, and Qingjiang Shi "Level set model for water region segmentation in synthetic aperture radar images," Journal of Applied Remote Sensing 13(2), 026510 (4 June 2019). https://doi.org/10.1117/1.JRS.13.026510

Received: 28 December 2018; Accepted: 13 May 2019; Published: 4 June 2019

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