Segmentation of polluted SSS image by combining NSCT and multifractal

doi:10.11947/j.AGCS.2020.20180276

Abstract

Abstract: To improve the accuracy of current segmentation algorithms for the side scan sonar image with high noise, a side scan sonar image segmentation method is proposed that comprehensively utilizing of decomposing image by NSCT (non-subsampled contourlet transform), enhancing image by combination of local standard deviation and mean, estimating the singularity of image by multi-fractal. Firstly, NSCT is used to decompose images to obtain low frequency image which filtered out high frequency noise and retain contour information and a series of high-frequency direction sub-band images. Then, based on the feature that target shadow appeared with the target in side scan sonar images, it is calculated that the low-frequency image feature combined the local standard deviation and mean to obtain the feature images that highlight the characteristics of the target and its shadow respectively, use the multifractal method to segment the feature image to get the result of low-frequency image segmentation. The image difference and non-maximal suppression methods are used to segment the high-frequency direction sub-band images and obtain the high-frequency segmentation results. Finally, it is obtained that the fine edge of the target and its shadow by combing of high and low frequency segmentation result. The validity of this method is verified by experiments.

Key words: side-scan sonar, image segmentation, combined feature, NSCT, multifractal

CLC Number:

P229

HE Yicai, ZHAO Jianhu, ZHANG Hongmei, RUAN Shilun. Segmentation of polluted SSS image by combining NSCT and multifractal[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2): 162-170.

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