Transactions of Nanjing University of Aeronautics & Astronautics
Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology
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Abstract:
To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar (SAR) images, an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology. The SAR image is firstly transformed to a contourlet domain. According to the directional information and gradient information of directional subband of contourlet transform, the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges, respectively. Subsequently, the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image. Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges, compared with the Canny method，the method based on wavelet and Canny, the method based on contourlet modulus maxima, and the method based on improved (ROEWA). The obtained river edges are complete and clear.
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Project Supported:
the CRSRI Open Research Program (CKWV2013225/KY); the Open Project Foundation of Key Laboratory of the Yellow River Sediment of Ministry of Water Resource (2014006); the Open Project Foundation of Key Lab of Port, Waterway and Sedimentation Engineering of the Ministry of Transport; the State Key Lab of Urban Water Resource and Environment (HIT) (ES201409); the Priority Academic Program Development of Jiangsu Higher Education Institution
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