51results about How to "Good regional consistency" patented technology

The invention discloses a Freeman decomposition and homo-polarization rate-based polarized synthetic aperture radar (SAR) image classification method for mainly solving the problems of higher calculation complexity and poor classification effect in the prior art. The method comprises the following steps of: (1) inputting a covariance matrix of polarized SAR data; (2) performing Freeman decomposition on the input matrix to acquire three types of scattering power matrixes of plane scattering, dihedral angle scattering and volume scattering; (3) performing initial division on the polarized SAR data according to the three types of scattering power matrixes; (4) calculating the homo-polarization rate of all pixel points of the polarized SAR data of each class; (5) selecting a threshold value, and dividing the polarized SAR data of each class in the step (3) into 3 classes according to the homo-polarization rate, so that the whole polarized SAR data are divided into 9 classes; and (6) performing repeated Wishart iteration and coloring on the division result of the whole polarized SAR data to obtain a final color classification result graph. Compared with the classical classification method, the method has the advantages that the division of the polarized SAR data is stricter, the classification result is obvious and the calculation complexity is relatively low.

The invention discloses a polarization synthetic aperture radar (SAR) image classification method based on spectral clustering. The polarization SAR image classification method mainly solves the problem that an existing non-supervision polarization SAR classification method is low in accuracy. The polarization SAR image classification method comprises the steps of extracting scattering entropy H of representation polarization SAR target characteristics to serve as an input characteristic space of a Mean Shift algorithm combining with space coordination information; diving in the characteristic space with the Mean Shift algorithm to obtain M areas; choosing representation points of all areas on the M areas to serve as spectral clustering input to spectrally divide all areas, and further finishing spectral clustering on all pixel points to obtain pre-classification results; and finally classifying the whole image obtained from the pre classification with a Wishart classifier capable of reflecting polarization SAR distribution characteristics in an iteration mode to obtain classification results. Tests show that the polarization SAR image classification method is good in image classification effect and can be applied to non-supervision classification on various polarization SAR images.

The invention relates to the field of image processing and discloses a multi-scale color texture image segmentation method combined with an MRF (Markov Random Field) and a neural network. The method is mainly used for solving the problem that a simple Gauss model can not be used for accurately describing a feature field distribution characteristic in the traditional MRF method. The method comprises the following steps: when modeling a multi-scale feature field, estimating a probability distribution of a texture feature by using the output of the neural network in a supervision environment on each scale; when modeling a multi-scale mark field based on a classic Potts model, considering the interaction of a mark set of a same-scale second-order neighborhood position and a corresponding mark on a lower resolution scale for each pixel position; by using the maximum likelihood criterion, gradually acting a segmentation result on the lower resolution scale on the next scale from top to bottom to finally realize the multi-scale image segmentation. The segmentation result obtained by the invention has good region homogeneity and Boundary authenticity, and the method can be used for segmenting the color texture image.

The invention discloses a K distribution and texture feature-based SAR (Synthetic Aperture Radar) image segmentation method, which belongs to the technical field of image processing. The segmentation method comprises the following steps: intercepting category C training samples on an SAR image to be segmented; intercepting 100 training samples of 9*9 at a feature region; extracting feature parameters of the training samples by using a K distribution statistical model and a gray scale co-occurrence matrix; arranging each category of feature coefficients into a matrix, i.e., a dictionary of cth category targets; calculating the dictionary of each category of targets and arranging the dictionaries to form a global big dictionary in sequence according to the method; inputting the SAR image to be segmented, substituting each pixel point by 9*9 pixel points of a neighbor of the pixel point, and solving a feature coefficient inverse solution matrix operation of the pixel point to obtain weight a; making delta i (a), i=1, ellipsis, wherein C is a vector which only remains the coefficient corresponding to the cth category in a and enable zero setting of the rest coefficients; and calculating a residual error function and repeating the steps to obtain a segmentation result of each pixel point of the SAR image to be processed according to the fact that the minimum error function is a category label of the feature coefficients.

The invention relates to an image segmentation method based on geometric block spacing symbiotic characteristics and semantic information. The method comprises the steps that an initial sketch model is used for obtaining a sketch image, geometric blocks are constructed by taking line segments forming a sketch line as a unit, then the geometric blocks are mapped to the corresponding position of an original image, spacing symbiotic matrixes based on the geometric blocks are extracted, the spacing symbiotic matrix of each geometric block is taken as the characteristics of the corresponding line segments, the characteristics are utilized for dividing the sketch line into the speckle semantic category and the general boundary category, semantic information classified by the sketch line is respectively utilized for guiding the image super pixels obtained by the over-segmentation method to be combined, statistic is carried out on color mean values of the super pixels which are combined by being guided by the sketch line with the speckle semantic category, according to the symbiotic statistic relation between each super pixel and the super pixel in the neighbourhood in color, further combining is carried out, and the final segmenting result is obtained.