Research On SAR Image Segmentation Algorithm Based On Fuzzy Clustering

Synthetic Aperture Radar(SAR)is one of late-model remote sensing detection methods.As an active microwave sensor,SAR can generate high-resolution images at any time and under severe weather conditions.It is an important branch of earth exploration in the field of remote sensing and has broad development space and application prospects.SAR image segmentation is usually based on the internal grayscale,texture and other attributes of the SAR image,combined with different technical means to divide the image into several distinct and non-overlapping sub-regions.As an important part of the practical application of SAR,it can simplify The SAR image content and separates the target from the complex background effectively,which is of great benefit to the subsequent analysis and processing of the SAR image.However,the coherent speckle noise in SAR images has brought great challenges to the image processing and interpretation process.Therefore,how to effectively suppress noise and achieve accurate image segmentation while retaining the true information of SAR images has gradually become a research hotspot in recent years.Fuzzy clustering algorithm is a soft partition algorithm that can divide pixels into different categories at the same time.It is very suitable for dealing with incomplete gray information and uncertain results in SAR images.Fuzzy C-Means(FCM)algorithm is an unsupervised fuzzy clustering method with better performance.In this paper,we do some research on SAR image segmentation based on the fuzzy clustering algorithm,and combine the FCM algorithm and the non-local information of the SAR image to propose two new algorithms.The algorithms are presented below.(1)A SAR image segmentation method combining non-local information and fuzzy clustering is proposed.Through the research of the existing fuzzy clustering algorithm,we found that introducting the neighborhood space information of the pixel can improve the anti-noise performance of the algorithm and improve the segmentation result of the noisy image.However,there may also be abnormal pixel values in the neighborhood space of the pixels in the severely noisy SAR image,which makes the suppression of noise by neighboring pixel information unable to achieve the desired effect.Firstly,the algorithm preprocesses the original image with Gaussian filtering in order to deal with the coherent speckle noise in the SAR image more effectively,then extracts the non-local information of the SAR image to improve the objective function of the fuzzy C-means algorithm that use the larger spatial information of the pixels to improve the robustness to noise.The initial segmentation of the image is achieved through iterative clustering,and then the final segmentation is completed using morphological operations.Finally,multiple experiments prove that the algorithm can achieve better suppression effects when dealing with coherent speckle noise of different intensities,and achieve high-precision segmentation of SAR images.(2)An improved SAR image segmentation algorithm based on adaptive non-local information and fuzzy clustering is proposed.First of all,in order to solve the problem of slow clustering process and unstable effect caused by the random initialization of the fuzzy C-means algorithm,the following clustering centers are pre-calculated by selecting the main pixels in the SAR image according to certain rules,which can speed up the execution efficiency of the algorithm.Secondly,in order to better preserve the edge details of the image while effectively suppressing the noise,an adaptive smoothing factor is proposed,which adjusts the degree of smoothness adaptively according to the characteristics of different regions,and then uses the adaptive non-local information to adjust the fuzzy C-means algorithm.The segmentation results of the SAR image are obtained through iteration and morphological processing.Finally,experiments on synthetic and real SAR images prove that the algorithm can significantly reduce the number of clustering iterations,and retains better while suppressing noise The details of the image,compared with other algorithms to obtain a higher segmentation accuracy.