Multiscale SAR image segmentation method based on semi-supervised learning

Abstract

The invention discloses a multiscale SAR image segmentation method based on semi-supervised learning, belonging to the technical field of image processing and mainly overcoming the disadvantages of low segmentation accuracy and relatively long operation time of the traditional segmentation methods. The implementation steps are as follows: (1) three-layer wavelet transform and three-layer Contourlet transform are respectively carried out on the images to be segmented to finish image decomposition and a coarse decomposition subband, a sub-coarse decomposition subband and a fine decomposition subband are obtained by merge operation; (2) with respect to the coarse decomposition subband, the method of semi-supervised learning is adopted to finish initial segmentation and obtain the results of initial segmentation; and (3) multiscale secondary segmentation based on unsupervised learning is carried out on the results of initial segmentation, the sub-coarse decomposition subband and the fine decomposition subband obtained in step (1) to obtain the final segmentation result. The method improves the accuracy of the segmented images, reduces the misclassification rate and can be used for texture image segmentation, natural image segmentation and medical image segmentation.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image segmentation method, which can be used for the segmentation of synthetic aperture radar images, namely SAR images, texture images, general natural images and medical images. Background technique [0002] Image segmentation is one of the key technologies in the field of image processing and computer vision. Its purpose is to divide the image into regions with different characteristics, and extract the objects of interest to provide the basis for subsequent classification, recognition and retrieval. The purpose of segmenting the SAR image is to effectively identify the target in the next step. SAR images contain some rivers, bridges, shrubs, cities, farmland, ports, etc. When dealing with these types of segmentation, it can be regarded as the segmentation of texture images, because these types of segmentation have certain structures, periods, direction etc. [0...

AI Technical Summary

Problems solved by technology

However, the classification results of the unsupervised learning method are unstable. For the clustering method, the segmentation accuracy will be affected to varying degrees due to the different selection of the center point each time.

Images