| Synthetic Aperture Radar(SAR)imaging system is widely used in remote sensing imaging field.Because of its inherent characteristics such as all-time,all-weather,high-resolution and abundant signals,SAR images provide more effective information than other remote sensing images.Therefore,SAR plays an important role in both military and civil fields.SAR images are different from other images in many aspects,and the speckle noise is also its inherent characteristic,which brings challenges to SAR image processing.As an important step in SAR image processing,SAR image segmentation can divide and simplify images to facilitate subsequent recognition,detection and some other steps.So that it help to promote the update and development of SAR image processing technology.The unsupervised pixel-level SAR image segmentation task studied in this thesis is to use SAR image information to divide it into connected but non-overlapping homogeneous regions,and the pixels within the same region belong to the same category.In the pixel-level SAR image segmentation task,the high computational complexity caused by excessive number of pixels and the common speckle noise in SAR image brings difficulties to the image segmenting process.This thesis perfoms research and experiments on these problems,and the main contents are as follows:(1)A new algorithm based on thumbnail and hierarchical fuzzy clustering is proposed for SAR image segmentation.Firstly,the major similar pixels in the pixel patches are used to generate a thumbnail of the image,whose size is obviously reduced compared with the input SAR image.Performing fuzzy clustering on the thumbnail can reduce the time consumed in the clustering operation.Secondly,the algorithm combines the similar neighbor information in the thumbnail to segment the thumbnail using fuzzy clustering algorithm,which also reduces the effect of speckle noise to some extent.After obtaining the clustering results of thumbnail,the label information of the thumbnail is used to perform hierarchical segmentation of the input SAR images,so as to make the segmentation results more accurate and uniform.(2)A new SAR image segmentation algorithm based on non-local superpixel and hierarchical fuzzy clustering is proposed.Firstly,the algorithm uses the similarity measure based on pixel patch to divide the input image into non-local superpixels,and extracts the major similar pixels in each superpixels to generate the thumbnail.Secondly,it performs fuzzy clustering on the superpixels combining with the similar neighbor information of the irregular window in the thumbnail.Finally,the input SAR image is segmented hierarchically using the label information of the superpixels.This algorithm aims to combine the idea of the non-local mean filtering and the superpixel,to reduce the repetitive calculating operation in the two steps,and improve the efficiency of SAR image segmentation on the premise of keeping the details of the image as much as possible.(3)A new algorithm based on double thumbnails and multi-objective fuzzy clustering is proposed to deal with the SAR image segmentation problem.Based on the predivided superpixels,the algorithm uses the image information of different scales to generate two low-resolution thumbnails.Then it combines the information of two thumbnail images into the objective function of fuzzy clustering by means of summation.An adaptive coefficient matrix is established to control the summation coefficient of two thumbnails in clustering operation.In addition,multi-objective fuzzy clustering is introduced to segment two thumbnail images,which is beneficial to further balance the conflicting demands of maintaining image details and reducing the impact of noise.Finally,the thumbnail segmentation result is used to traverse the pixels in the original image for three times to determine the labels of the input SAR image.This algorithm makes full use of the image information of different scales and improves the robustness to the inherent speckle noise. |
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