River Channel Extraction in SAR Images Using Level Sets Driven by Symmetric Kullback-Leibler Distance

This paper proposes a novel level set method (LSM) that improves river channel extraction accuracy in synthetic aperture images (SAR) by developing global median image fitting energies. First, we define a new global median fitting image (GMFI) to approximate the input image and use this GMFI to construct the fitting energy based on the symmetric Kullback-Leibler distance (SKL). Second, to exploit more image grayscale features, a squared global median fitting image (SGMFI) is derived and another fitting energy is similarly constructed using this SGMFI based on SKL. Third, we integrate the above two fitting energies and introduce additional regularized energies. The proposed LSM is verified and compared with several state-of-the-art methods on real SAR images. The river channel extraction results indicate that our proposed LSM has a clear advantage in accuracy and is robust to level set initialization.