217 results about "Sar image segmentation" patented technology

The invention discloses a SAR (Synthetic Aperture Radar) image segmentation technique based on dictionary learning and sparse representation, and mainly solves the problems that the existing feature extraction needs a lot of time and some defects exist in the distance measurement. The method comprises the following steps: (1) inputting an image to be segmented, and determining a segmentation class number k; (2) extracting a p*p window for each pixel point of the image to be segmented so as to obtain a test sample set, and randomly selecting a small amount of samples from the test sample set to obtain a training sample set; (3) extracting wavelet features of the training sample set; (4) dividing the training sample set by using a spectral clustering algorithm; (5) training a dictionary by using a K-SVD (Kernel Singular Value Decomposition) algorithm for each class of training samples; (6) solving sparse representation vectors of the test sample on the dictionary; (7) calculating a reconstructed error function of the test sample; and (8) calculating a test sample label according to the reconstructed error function to obtain the image segmentation result. The invention has the advantages of high segmentation speed and favorable effect; and the technique can be further used for automatic target identification of SAR images.

A level set polarization SAR image segmentation method based on polarization characteristic decomposition, belonging to the radar remote sensing technology or the image processing technology. In the invention, a polarization characteristic vector v which is composed of three polarization characteristics: H, alpha and A is obtained by the polarization characteristic decomposition of each pixel point of the original polarization SAR image; the polarization characteristic vectors v of all the pixel points are combined into a polarization characteristic matrix omega so as to convert the segmentation problem of the polarization SAR image from data space to polarization characteristic vector space; and the condition that the characteristic vector definition is suitable for energy functional of the polarization SAR image segmentation is utilized and a level set method is adopted to realize the numerical value solution of partial differential equation, thus realizing the polarization SAR image segmentation. The method provided by the invention takes full use of the polarization information of the polarization SAR image; therefore, the image edge obtained by segmentation is relatively complete so that the local characteristic is maintained better, the robustness for noise is stronger, the stability of the arithmetic is higher and the segmentation result is accurate; and the invention reduces the complexity of data and can effectively improve the image segmentation speed.

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on parallel sparse spectral clustering, relating to the technical field of image processing and mainly solving the problem of limitation of segmentation application of large-scale SAR images in the traditional spectral clustering technology. The SAR image segmentation method comprises the steps of: 1, extracting features of an SAR image to be segmented; 2, configuring an MATLAB (matrix laboratory) parallel computing environment; 3, allocating tasks all to processor nodes and computing partitioned sparse similar matrixes; 4, collecting computing results by a parallel task dispatcher and merging into an integral sparse similar matrix; 5, resolving a Laplacian matrix and carrying out feature decomposition; 6, carrying out K-means clustering on a feature vector matrix subjected to normalization; and 7, outputting a segmentation result of the SAR image. The invention can effectively overcome the bottleneck problem in computation and storage space of the traditional spectral clustering technology, has remarkable segmentation effect on large-scale SAR images, and is suitable for SAR image target detection and target identification.

The invention discloses an SAR image segmentation method based on wavelet pooling convolutional neural networks. The SAR image segmentation method comprises 1. constructing a wavelet pooling layer and forming wavelet pooling convolutional neural networks; 2. selecting image blocks and inputting the image blocks into the wavelet pooling convolutional neural networks, and training the image blocks; 3. inputting all the image blocks into the trained networks, and testing the image blocks to obtain a first class mark of an SAR image; 4. performing superpixel segmentation of the SAR image, and blending the superpixel segmentation result with the first class mark of the SAR image to obtain a second class mark of the SAR image; 5. obtaining a third class mark of the SAR image according to a Markov random field model, and blending the third class mark of the SAR image with the superpixel segmentation result to obtain a fourth class mark of the SAR image; and 6. blending the second class mark of the SAR image with the fourth class mark of the SAR image according to an SAR image gradient map to obtain the eventual segmentation result. The SAR image segmentation method based on wavelet pooling convolutional neural networks improves the segmentation effect of the SAR image and can be used for target detection and identification.

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on multi-scale feature fusion, comprising the steps of: firstly, extracting the textural features of an image by utilizing fast discrete curvelet transform (FDCT), extracting the statistic features of the image by utilizing stationary wavelet transform (SWT), then, fusing the two type of multi-scale features into high-dimensional feature vectors, and finally, segmenting by adopting a fuzzy C-mean clustering method. At the same time of eliminating broken pieces in a homogenizing area, the boundary is more precise and smoother. The method is favorable for effectively preventing the extraction of textural information from the interference of speckle noises. By fusing SWT coefficient statistical features, the textural information and the grey statistical information of the SAR image are effectively utilized and a favorable segmentation effect can be achieved.

The invention discloses an SAR (synthetic aperture radar) image segmentation method based on depth autoencoders and area charts, and mainly solves the problems of inaccurate and careless segmentation in the prior art. The method comprises the following steps: 1, obtaining an SAR image sketch according to an initial sketch model, complementing sketch segments to obtain the area charts, mapping the area charts to an original chart to obtain gathering, homogeneous and structure areas; 2, training the gathering and homogenous areas by the different depth autoencoders respectively to obtain expressions corresponding to all points, and cascading last two encoding layers to serve as the characteristics of the points; 3, constructing dictionaries for the gathering and homogenous areas respectively, projecting the characteristics of the points to the corresponding dictionaries, and gathering area characteristics of sub-areas; 4, clustering the sub-area characteristics of two types of areas respectively; 5, segmenting the structure area by superpixel binning under the guidance of the sketch segments; 6, combining the segmentation results of the areas to finish SAR image segmentation. The method has the advantages of accurate and careful segmentation, and can be used for target identification.

The invention discloses a high-resolution SAR image individual building extraction method, and the method comprises the steps: firstly carrying out domain ontology modeling through combining the imaging features of a high-resolution SAR image individual building and the characteristics of a complex morphological structure, and constructing an individual building body semantic model in a high-resolution SAR image; secondly obtaining image regions with the good homogeneity and clear boundary through employing SAR image segmentation based on an object, wherein the image regions are the basic processing units extracted by building base units; combining with the related rule of the building base units in the body model, wherein the extracted image object characteristics comprise regional features, shape features, geometric features, texture features, and topological features; forming the object expression of body semantic description according to the body semantic rule and the object features, guiding the image objects to be automatically recognized as the building base units, and achieving the large-scale individual building recognition which takes the semantic knowledge as the center. The method can extract a large-scale individual building in the high-resolution SAR image accurately and quickly.

The invention discloses a SAR image segmentation method based on a ridgelet deconvolution network and sparse classification and mainly solves a problem that image features are extracted in virtue of human experience in the prior art. The method comprises following steps of: (1) sketching a synthetic aperture radar (SAR) image, (2) dividing the SAR image into different semantic regions; (3) training a ridgelet deconvolution network (RDN) of aggregation regions and a RDN of homogeneous regions; (4) merging similar aggregation regions; (5) merging similar homogeneous regions; (6) based on a watershed method, segmenting a structural region acquired in the step (2), and (7) acquiring the segmented SAR image. The SAR image segmentation method achieves good region consistency, improves the segmentation effect of the SAR image segmentation, and can be used for target detection and identification.

The invention discloses an SAR (stop and reveres) image segmentation method based on dictionary migration clustering, which mainly solves the problems that the existing artificial mark SAR image has high cost and the existing non-mark SAR image can not assist a target SAR image in segmenting. The method has the following realization processes: 1) extracting wavelet characteristics for the target SAR image and the non-mark assistant SAR image; 2) setting circulation ending times, and preliminarily dividing the target SAR image with a k-means method; 3) training a dictionary for each class of target SAR image data; 4) migrating a group of samples for each class of target SAR image data from the assistant SAR image data; 5) removing the assistant data sample with an unstable label by a spectral clustering integration method; 6) training an assistant dictionary by each bath of purified assistant samples; and 7) updating a sample label and outputting a clustering segmenting result according to a target dictionary, the assistant dictionary and a corresponding clustering center. The SAR image segmentation method has the advantage of good segmenting effect and can be used for further identifying the SAR image target.

The invention discloses a level set SAR (Synthetic Aperture Radar) image segmentation method by combining edges and a regional probability density difference, belonging to the technical field of image processing and mainly solving the problems of difficult segmentation of SAR images with fuzzy edges and inaccurate positioning to real edges of the SAR images, of the traditional level set method. The method comprises the following implementation steps of: firstly, detecting an edge intensity modulus absolute value of Rmax of an SAR image by applying an ROEW operator; secondly, initializing a level set function phi, segmenting the SAR image into an inner region omega1 and an outer region omega2, and solving for the intensity mean values c1 and c2 of the two regions; thirdly, solving for the estimated probability densities of the two regions omega1 and omega2 according to c1 and c2 and calculating the actual probability densities p1 and p2 of the two regions; and fourthly, constructing a total energy function ESAR, solving for a gradient downstream equation by applying a variational method, and updating the level set phi to obtain new segmentation regions omega1 and omega2. Indicated by experimental results, the segmentation method can be used for obtaining more ideal segmentation effect and be used for the edge detection and the target identification of the SAR images.

The invention discloses a method for segmenting a heterogeneous super-pixel SAR (Synthetic Aperture Radar) image based on Gamma distribution, used for mainly solving the problems of incomplete detailed information and disordered boundaries easily due to super-pixel pre-segmentation of the SAR image. The method comprises the following steps: performing super-pixel pre-segmentation of the image; estimating the heterogeneity of each super-pixel by using Gamma distribution, and finding out a wrongly segmented super-pixel block due to the weak boundary of the super-pixel; dividing wrongly segmented super-pixels into two kinds by using Kmeans; extracting features of each point in the original image, and extracting image texture features by adopting a gray co-occurrence matrix so as to obtain four-dimensional gray co-occurrence matrix features; extracting wavelet features of the original image so as to obtain nine-dimensional gabor features; distributing equal weights to the image texture features and scattering features, and fusing feature combinations; averaging pixel features in the super-pixel block so as to obtain features of each super-pixel; and classifying by using a Gaussian mixture model so as to obtain a final segmentation result. According to the invention, heterogeneous super-pixels can be segmented effectively; complete detailed information is reserved; the consistency of homogeneous regions is good; and noise is effectively inhibited.

The invention discloses an SAR image segmentation method based on feature extraction and cluster integration. The SAR image segmentation method mainly solves the problem that sensitivity of paraphase speckle noise and segmentation accuracy in an existing method are low. The SAR image segmentation method comprises the following steps that (1) feature extraction is conducted on an original SAR image, a multi-dimensional feature set is constructed, and dimensionality reduction is conducted on the multi-dimensional feature set so as to obtain a new feature set; (2) repeated selective Kmeans clustering is conducted on the new feature set so as to obtain a plurality of clustering center sequences, and center matching is conducted on the clustering center sequences; (3) by means of the matched clustering center sequences, the new feature set is divided so as to obtain a plurality of mark vectors; (4) the obtained mark vector are integrated to obtain an integrated mark vector; (5) by means of the integrated mark vector, a segmentation result of the SAR image is obtained. The SAR image segmentation method has the advantages of having high paraphase speckle noise robustness and high segmentation accuracy and can be used for target detection and recognition of the SAR image.

The invention discloses an improved watershed SAR image segmentation method based on complex wavelet texture region merging, which combines a complex wavelet feature extraction method and the watershed algorithm improved by the Gaussian low-pass filter to be used for restraining over segmentation existing in watershed-segmented SAR images. The method comprises the following implementation steps of: (1) carrying out the improved watershed transformation to an original image so as to obtain initial segmentation; (2) extracting complex wavelet energy feature of each pixel in the initial segmented image; (3) unifying the pixel feature in each irregular region block; and (4) sorting and merging different region blocks by utilizing a 'K-mean value' clustering method to reduce the region blocks and obtain final results. The image segmentation effect of the method is verified to be basically consistent with the standard by a group of simulation experiments.

The invention discloses an SAR image segmentation method based on semantic information classification. The SAR image segmentation method based on the semantic information classification mainly solves the problem that ground object zones, formed by uniformly connective ground object target gathering, of a forest, a building group and the like can not be obtained through non-supervision segmentation by an existing segmentation method. The method comprises the following steps: (1) an initial sketch model is used on an input SAR image so that an initial sketch image expressing image structure information is obtained; (2) semantic information analysis is performed on the initial sketch image so that semantic information classification results of all line segments are obtained; (3) the ground object zones formed by the ground object target gathering are classified based on the semantic information analysis; and (4) the rest zones are divided into zones to be determined and non-line-segment zones and SAR image segmentation is respectively performed to the zones to be determined and the non-line-segment zones so that the SAR image segmentation is finally achieved. Compared with the prior art, the SAR image segmentation method based on the semantic information classification is strong in generality and capable of achieving segmentation of SAR images with a large amount of ground object zones formed by the ground object target gathering. Uniform connectivity of a segmentation result is good, edge location is accurate, and the independent ground object target can be segmented.

The invention discloses an SAR image segmentation method based on a deconvolution network and sketch direction constraint. The invention mainly solves the problem that existing segmentation technologies are inaccurate in SAR image segmentation. The method comprises the implementation steps of 1, sketching an SAR image, thus acquiring a sketch; 2, dividing pixel sub-spaces of the SAR image according to an area chart of the SAR image; 3, training the deconvolution network; 4, clustering filter directions; 5, segmenting the pixel sub-space with a hybrid aggregation structure by using the SAR image segmentation method based on the deconvolution network and the sketch direction constraint; 6, performing independent target segmentation based on a sketch line gathering characteristic; 7, performing line target segmentation based on a visual semantic rule; 8, segmenting the homogeneous area pixel sub-space by using a polynomial-based logistic regression prior model; and 9, combining segmentation results, thus acquiring an SAR image segmentation result. According to the method provided by the invention, the good segmentation effect of the SAR image is acquired, and thus the method can be used for performing semantic segmentation on the SAR image.

The invention relates to an image segmentation method having multi-layer segmentation networks based on Bayes framework with edge prior information. The method herein includes the following steps: S1.performing general classification on input data by using a full convolutional neural network, outputting scoring graphs of all types that have the same sizes of the input data, also extracting an implicit edge image from an internal feature layer of the full convolutional neural network; S2. extracting an explicit edge image from the input data by using an edge detection network; S3.performing first restraining on the types that are obtained from S2 by using domain transformation and conditional random field, obtaining an initial segmentation image; S4. transforming the explicit edge image obtained from S2 to an edge distance image; and S5. inputting the edge distance image to defined domain transformation, performing second edge restraining on the initial segmentation image that is obtained from S3, and obtaining a final segmentation result. According to the invention, the method can extract edge prior information through an external edge network and performs edge region segmentationand filtering on the result from the general segmentation by using the edge prior information, so that the method herein can increase the accuracy in segmenting a SAR image.

The invention discloses a level set SAR (Synthetic Aperture Radar) image segmentation method based on local and global area information, which mainly solves the problem that the existing level set method is influenced by speckle and cannot segment the SAR images with uneven gray. The method comprises the following implementation steps of: firstly, initializing a level set function phi, and segmenting the SAR image into an internal area omega 1 and an external area omega 2; secondly, convolving intensity information of the internal area and the external area of the image through a Gaussian Kernel Function, taking the convolved information as local area information, and forming an energy term based on the local area; then, solving intensity mean values c1 and c2 and probability densities p1and p2 of the internal area and the external area, and forming the energy term of the global area; and finally, adding a bound term L (phi) of level set length and a penalty P (phi) which avoids renewed initialization, forming a total energy function ESAR, solving a gradient sinking equation through a variation method, and updating the level set phi. The obtained new segmented area and the experimental result show that the segmentation method provided by the invention can get more ideal segmentation effects, and the method can be used for SAR image segmentation and target identification.

The invention discloses a segmentation method for SAR images in consideration of multi-feature multi-scale MRF field, which is used for the treatment of remote sensing images, the method comprises the steps of: (1) extracting texture information from original images by utilizing a gray level co-occurrence matrix, and performing multi-scale segmentation on the resultant texture characteristic images; (2) detecting edge information of the original images by means of wavelet transformation in order to obtain edge characteristic images; (3) incorporating texture multi-scale segmentation result with edge characteristics by a linear weighting method, namely, corresponding pixel points of the two images are directly subjected to weighted stack to obtain final segmentation result. The invention reduces erroneous segmentation rate of SAR images and outstandingly remains detailed information of the images like edge information while eliminating speckle noise.

The invention discloses a method for segmenting three Markov field SAR image based on Gabor characteristic, which is realized by the following steps of: (1) inputting SAR image; (2) initializing label field; (3) building additional field; (4) obtaining united prior probability; (5) building segmentation model of posterior edge; (6) maximizing probability segmentation of posterior edge; (7) updating the label field; (8) judging if change rate of the label field is more than threshold valve; and (9) outputting final segmentation result. Compared with segmentation method of multi-dimension Markov model, this method simplifies segmentation process, ensures area uniformity of segmentation results, better keeps edges of different areas in the images, and solves the problem on unclear edges and poor area uniformity in the image segmentation problem. The method can be applied to synthesized aperture radar SAR image segmentation and SAR image target identification.

The invention discloses a two-dimensional dual-threshold SAR image segmentation method based on quantum particle swarm optimization. The method comprises the realization steps: (1) initializing the particle swarm population size M and the maximum iterations Tmax and generating the initial position of each particle randomly; (2) calculating a fitness function, and obtaining the optimal position of a current particle and an overall optimal position of iteration of this time according to the maximum in-cluster variance value; (3) calculating Pid and mbestd; (4) constructing a random number set; (5) setting a definition value, judging the relation between the random number set and the definition value, and upgrading the position of each particle according to a formula; (6) checking whether the end condition is achieved, achieving the end if yes, or else executing the steps from two to five; (7) segmenting an SAR image according to a pair of searched optimal thresholds stored in two dimensions of the particle pointed by the overall optimal position. Compared with a classical segmentation method, the segmentation method is good in effect of dividing the SAR image, and relatively small in time complexity.

The invention discloses an SAR image segmentation method based on a shape completion area chart and feature coding. The method solves the technical problem of closure extraction of SAR image areas. The method includes the steps that a sketch is obtained by utilizing an SAR image initial sketch model; the area chart is obtained by utilizing the area chart completed through sketch lines and based on shapes; the area chart is mapped into an original SAR image space, and then a clustering area, a homogeneous area and a structural area are obtained; characteristic learning and hierarchical clustering are carried out on the clustering area; characteristic learning and hierarchical clustering are carried out on the homogeneous area; segmentation is carried out on the structural area; results of the clustering area, the homogeneous area and the structural area are combined, line targets are marked, and a final SAR image segmentation result is obtained. According to the method, the homogeneous area with the better closure and homogeneity can be extracted, further clustering of the clustering area and the homogeneous area can be well achieved in the characteristic learning and clustering methods applied to the areas, and the method can be used for SAR image segmentation.

The invention discloses a multichannel SAR-GMTI method based on target shape and shadow assistance. The ideal of the method is that multichannel self-adaptive clutter suppression and cell average-constant false alarm rate (CA-CFAR) detection are performed on M-1 SAR images after image registration and channel equalization and an original SAR image of a reference channel so that a binary image is obtained; then clustering processing is performed on the binary image so that all potential moving targets and size information and position information thereof are obtained; then shadow size matching templates corresponding to the potential moving targets are calculated according to the established moving target shadow geometric calculation model and the acquired potential moving target size information, and shadow position matching templates are generated by utilizing radial speed estimation values and the position information of the potential moving targets; and finally the two shadow templates are respectively matched according to the size information and the position information of the segmented shadow areas of the SAR images, false moving targets without matching of shadow areas are removed, and thus false alarm is reduced.

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.

The invention discloses an SAR (specific absorption rate) image registration method based on straight lines and area, and mainly solves the problems of poor registering effect and high calculating complexity in the prior art. The SAR image registration method including the implementing steps of (1) inputting two SAR images and detecting straight lines in the two images through Hough conversion; (2), grouping the detected straight lines according to slope to acquire principal directions of the two SAR images, and calculating rotary parameter; (3), processing standard images and rotated images to be registered by the aid of an optical threshold segmentation method and through morphology corrosion and expansion to acquire a closed area; (4), utilizing an area centroid to calculate translation parameters; and (5), coinciding the standard images and the translationally rotated images to be registered to finish registration. The SAR image registration method based on the straight lines and area has the advantages that the SAR image registration method has small noise effect on registration of SAR images, is stable in SAR image segmentation results in a small water area and good in registration effect, high in speed and low in calculating complexity, and can be used for mode recognition, automatic navigation, computer vision and remote sensing image processing.

The invention discloses a local Gamma fitting-based active contour synthetic aperture radar (SAR) image segmentation method. The SAR image segmentation precision is low in the prior art. The method comprises the following steps of: representing the local structure of each pixel in an original SAR image in a certain neighborhood, regarding a maximum likelihood as a regional separation criterion, and defining a local fitting energy term; then integrating all pixel points in the defined region to acquire an overall optimized energy functional; and finally, describing and solving an energy minimization process by adopting a curve evolution theory and level set method-based geometrical active contour model so as to realize effective segmentation of the SAR image. The method has strong speckle noise inhibiting capability, can realize effective segmentation of the SAR image, and particularly has accurate segmentation effect on complex boundaries such as deep indentation and the like.

The invention discloses an SAR image segmentation method on the basis of the HMT model in the Shearlet domain, which pertains to the technical field of image processing and mainly aims at solving the problem that the application of the traditional multi-scale geometrical analysis in SAR image segmentation is easy to result in poor regional uniformity and disorder edges. The segmentation process comprises the steps of extracting feature areas (I0, I1, and the like, and IC) in the SAR image to be segmented, calculating the Shearlet transformation coefficients (S0, S1, and the like, and SC) of the feature areas, utilizing the EM algorithm to obtain the HMT model parameter set (Theta1, Theta2, and the like, and ThetaC) in the Shearlet domain of various feature areas, carrying out Shearlet transformation to the SAR image to be segmented to obtain an image coefficient S, utilizing the feature coefficients (S0, S1, and the like, and SC) to calculate likelihood values (Lhood, Lhood, and the like, and Lhood) corresponding to the SAR image coefficient S in each scale, calculating initial segmentation results (MLseg, MLseg, and the like, and MLseg) of the likelihood values in each scale according to the maximum likelihood rule, carrying out fusion to the initial segmentation results by maximizing a posteriori probability criterion and taking the fused image of the scale at the first level as a final segmentation result. The method has the advantages of high convergence rate, good regional uniformity of segmentation result and completely retained information, and can be applied to SAR image target identification.

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on Treelets and fuzzy C-means clustering, mainly solving the problem that target identification cannot be manually carried out on a large quantity of images in the prior art. The SAR image segmentation method comprises the steps of: 1, extracting texture features and wavelet features of an input SAR image to be segmented; 2, forming the texture features and the wavelet features into a feature matrix; 3, carrying out Treelets conversion on the feature matrix to obtain a basis matrix; 4, multiplying the feature matrix with a scaling function of the basis matrix to obtain a structural vector; and 5, carrying out fuzzy C-means clustering on the structural vector to obtain a segmentation result of the SAR image. The invention can be used for reducing dimensions of high-dimensional data before clustering by utilizing the Treelets conversion, can effectively inhibit the noise and improve the segmentation speed of the SAR image, and is used for automatic segmentation on the SAR image for carrying out target identification.

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.

The invention discloses an active contour synthetic aperture radar (SAR) image segmentation method based on Fisher distribution, which mainly aims to overcome the disadvantage of existing Gamma distribution to the SAR image segmentation technology, the method comprises the following specific implementation steps of (1) making use of Fisher distribution to fit the intensity statistical characteristics of an image area, and establishing an energy functional function based on the Fisher distribution according to a regional competition model; (2) introducing a level set function, and combining a length constraint item and a level set rule item to re-express the energy functional function obtained in step 1; (3) minimizing the energy functional function obtained in step 2 by adopting Euler-Lagrange calculus of variations, estimating Fisher distributed parameters by making use of logarithmic moment estimation, and then performing numerical solution to a partial differential equation, thereby obtaining the segmentation result of an SAR image. According to the method provided by the invention, the level set method evolutionary split curve and Fisher distributed parameter estimation are combined to minimize the energy functional function, thereby realizing segmenting SAR images.

The invention discloses an SAR image segmentation method based on feature vector integration spectral clustering and mainly provides a solution to the problems of low accuracy and poor scale parameter sensitivity appeared when an existing spectral clustering method is used in SAR image segmentation. The realization process is as follows: (1) carrying out three-layer wavelet transformation on an input SAR image and extracting wavelet features; (2) setting scale parameters sigma to be in the range from 0.1 to 1 and setting the number of integration features to m; (3) randomly selecting m scale parameters from the scale parameters sigma and using approximation to calculate m similarity matrixes W1, W2, ..., Wm; (4) calculating Laplasse matrixes L1, L2,..., Lm according to the m similarity matrixes W1, W2,..., Wm and respectively carrying out non-negative matrix factorization to obtain feature vector matrixes V1, V2,..., Vm; (5) integrating the feature vectors V1, V2,..., Vm to obtain a new feature vector matrix U; and (6) standardizing the feature vector matrix U to obtain Y, carrying out K_means clustering on the Y and outputting the final segmentation result of the SAR image. The SAR image segmentation method based on feature vector integration spectral clustering has the advantages of high-accuracy segmentation result and insensitive scale parameter and can be used in target detection and target segmentation and recognition of SAR images.