It is of great difficulty to utilize the existing active contour models (ACMs) to achieve accurate segmentation of synthetic aperture radar (SAR) river images. To address this problem, a novel ACM driven by J-divergence entropy is proposed. The external energy constraint term of the proposed model is defined by the J-divergence entropy, which differs from those of many existing ACMs defined by the Euclidean distance. Moreover, the median absolute deviations of pixel grayscale values inside and outside the curve are utilized as energy weights, which can adaptively adjust proportions of region energies inside and outside the curve, leading to the improvement in segmentation efficiency. Experiments are performed on a large number of SAR river images, and the results demonstrate that, compared with the existing ACMs, the proposed model shows clear advantages in terms of both segmentation performance and segmentation efficiency.
Image segmentation SAR river image Active contour model J-divergence entropy Median absolute deviation
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This work is partially supported by the National Natural Science Fund of China under Grant 61573183, Key Laboratory of Yellow River Sediment of Ministry of Water Resources under Grant 2014006, Engineering Technology Research Center of Wuhan Intelligent Basin under Grant CKWV2013225/KY, State Key Laboratory of Urban Water Resources and Environment under Grant LYPK201304.
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Conflict of interest
The authors declare that they have no conflict of interest.
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