SAR image segmentation method based on superpixels and optimizing strategy

Abstract

The invention discloses an SAR image segmentation method based on superpixels and an optimizing strategy. The SAR image segmentation method is mainly used for improving the phenomenon that areas segmented through an existing image segmentation method are poor in consistency. The SAR image segmentation method is realized through the following steps: (1) an SAR image is input and subjected to two times of non-downsampling wavelet transformation; (2) superpixel blocks of the input image are extracted; (3) wavelet features of the superpixel blocks are calculated; (4) an image matrix of the superpixel blocks is established; (5) the superpixel blocks are clustered according to the wavelet features of the superpixel blocks; (6) particle swarm optimization is adopted for optimizing parameters in the clustering process; (7) category labels of the superpixel blocks are calculated according to a membership matrix obtained after the optimization; (8) the corresponding category labels are marked on the boundaries between the superpixel blocks to obtain a segmentation result of the SAR image. The SAR image segmentation method based on the superpixels and the optimizing strategy can guarantee complete edge detail information and well guarantee the consistency of the segmented areas at the same time, and the segmentation result meets the requirement for follow-up image analysis.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to a SAR image segmentation method, and can be applied to preprocessing of image processing and recognition. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. It is a key step from image processing to image analysis. The existing image segmentation methods are mainly divided into the following categories: threshold-based, region-based and edge-based segmentation methods, and segmentation methods based on specific theories. Now, researchers continue to improve the original image segmentation method and apply some new theories and new methods of other disciplines to image segmentation, propose many new segmentation methods, and add various optimization algorithms and intelligent algorithms to image segmentation. Come on, improve the quality of t...

AI Technical Summary

Problems solved by technology

However, the processing of pixels is inefficient, and the pixel position and characteristics of the neighborhood window are used to weight the pixels, and the similarity between the spatial position and the pixel is not considered accurately, and the segmentation effect is slow and the consistency of the segmentation area is poor.

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