Edge detection for side-scan sonar images based on improved Canny operator

To deal with strong speckle noise of side-scan sonar images, an edge detection method based on improved Canny operator is proposed. According to a multiplicative model and the Rayleigh distribution of speckle noise, sonar image de-speckling is performed locally and adaptively in the contourlet transform domain without sub-sampling. This effectively suppresses speckles and better protects edges without blurring due to Gaussian smoothing. Gradients of the de-speckled sonar image are computed. The maximal magnitudes of the gradients are obtained by non-maximum suppression. The maximal points are classified into three types: strong edge points, weak edge points and non-edge points. Two thresholds are automatically determined based on the maxima of inter-class variance. A binary edge map is obtained with the two thresholds followed by weak edge linking. Experiments on both synthetic and real sonar images show that the proposed method has the advantages over other methods such as Canny operator and wavelet modulus maxima in terms of edge integrity, positioning accuracy and false edge points.