49 results about "Lee filter" patented technology

The invention puts forward a polarized SAR (Synthetic Aperture Radar) image classification method based on an SLIC (Software Licensing Internal Code) and an improved CNN (Convolutional Neural Network), and is used for solving the technical problem of low classification speed and low classification accuracy in an existing supervised polarized SAR image classification method. The method comprises the following steps that: firstly, taking the Wishart distance and the polarization feature of a polarized SAR image as new data, carrying out Lee filtering on the new data, and inputting the new data into an improved CNN to be classified so as to obtain a preliminary classification result; then, carrying out SLIC superpixel segmentation on the pseudo-color image of the polarized SAR image to obtain a superpixel segmentation result; and finally, utilizing the superpixel segmentation result to carry out constraint post-processing on the preliminary classification result to obtain a final classification result. The method is high in classification speed and accuracy, and can be used for fields including polarized SAR terrain classification and the like.

The invention discloses a polarization SAR image classification method based on RBM and SVM, mainly solving the problem of the existing polarization SAR image classification method that the classification precision is low. The method comprises the steps as follows: (1) inputting the polarization SAR image to be classified, having delicate polarization Lee filtering operation; (2) resolving and extracting original feature of each pixel point and integrating based on the polarization coherence matrix, polarization covariance matrix and Cloude; (3) initializing and training RBM to obtain the related parameter; (4) classifying by using SVM according to the feature learned by the RBM to obtain the classification result. Compared with the existing method, the space dependency of the image is totally considered and the feature in favour of classifying polarization SAR image can be extracted, the polarization SAR image classification precision is obviously raised, the method can be used for terrain classification and object identification for polarization SAR image.

A semi-supervised polarization SAR terrain classification method based on full convolution GAN is implemented by the following steps: performing refined Lee filtering; inputting polarization SAR data;constructing a generation network G; constructing a discrimination network D; training and testing the networks and calculating classification accuracy rate. Compared with the semi-supervised Co-training classification method in the prior art, the invention improves the classification accuracy of polarization SAR data of the polarized synthetic aperture radar, solves the problem of insufficient utilization of polarization information, enhances the robustness of the model to the input data, and has the effect of end-to-end classification. The invention can be applied to the terrain classification of polarized synthetic aperture radar polarized SAR data.

The invention discloses a polarimetric SAR (synthetic aperture radar) image segmentation method based on a DBN (deep belief network). The advantages of learning features of the deep learning theory are applied to the polarimetric SAR image segmentation. The segmentation method includes subjecting polarimetric SAR data to fine Lee filtering; subjecting polarimetric SAR coherence matrix T to H/Alpha resolving to obtain the parameter feature; extracting gray-level co-occurrence matrixes from three channels on leading diagonals of the coherence matrix T, and calculating features including contrast, coherence, energy and inverse difference; combining the features and the elements of the coherence matrix to train a two-layer DBN; inputting the polarimetric SAR data in the DBN for classification and displaying a classification result image. According to the arrangement, the scattering feature and the gray-level co-occurrence matrix feature are integrated, information integrity is kept, and the layer-by-layer learning feature is applicable to polarimetric SAR image object recognition.

The invention discloses a method for detecting oil spill at sea based on a full-polarized synthetic aperture radar (SAR) image. The method includes the following steps: pre-treating polarized SAR data which require analyzing to obtain a full-polarized SAR covariance matrix; conducting refining polarized Lee filtering on the covariance matrix; based on the filtered covariance matrix or through a Stokes vector calculated by the covariance matrix, extracting 6 polarized features to constitute a feature combination; inputting training sample features with ground verification label information to a maximum likelihood (ML) classifier, training the classifier and optimizing parameters of the classifier; taking polarized features as input, utilizing the ML classifier to conduct detection and classification on an oil membrane; processing a classification result based on morphology, utilizing verification information of actual measurement to evaluate classification precision. According to the invention, the method can increase performances of oil spill at sea detection and classification methods and promote the application of the full-polarized SAR in actual engineering problems such as monitoring of oil spill at sea.

The invention provides a polarity SAR target detection method based on a FCN-CRF master-slave network. The method comprises steps of inputting a to-be-detected polarity SAR image, and carrying out delicate polarity Lee filtering on a polarity coherence matrix T of the polarity SAR image to filter coherent noise so as to obtain the filtered coherence matrix, wherein each element of the filtered coherence matrix is a 3*3 matrix, that is to say, each pixel point has nine-dimensional features. According to the invention, by expanding image block features into pixel-level features, the correlation degree of selected training samples through matching of pixel points of a region of interest is quite high and quite effective; the feature image blocks with the pixel points of the region of interest whose quantity is less than 50% of the whole image block will not participate in following calculation, so the operand is greatly reduced and the detection efficiency is improved; by using the Lee filtering to pre-process of the original polarity SAR image, coherence spot noise is effectively reduced and image quality and detection performance are improved; and by use of spiral scattering components corresponding to urban buildings obtained through the Yamaguchi decomposition, features of polarity SAR artificial targets are effectively extracted, and detection precision of the artificial targets is improved.

The invention discloses a construction method of document image enhancement, which is characterized by comprising the following steps of: 1. combining global contrast enhancement and local contrast enhancement together, i.e. combining a CLAHE (Contrast-Limited Adaptive Histogram Equalization) method and an Lee filter method, and forming a detail-strengthened AHE method; 2. improving a DAHE method, and introducing a gradient amplitude to enable the DAHE method to have image space self-adaptive ability and obtain an IDAHE method; and 3. carrying out experiments by using sampled data to obtain the optimal experimental numerical values of parameters epsilon, k, lambda1 and lambda 2 of input images in different types. The invention has the advantages that the global contrast is improved, the local details are effectively amplified, and imaging is clear.

The invention discloses an ice cover radar image ice layer fine segmentation method based on an FCN-ASPP network, and relates to the field of computer vision and mode recognition. According to the invention, the radar amplitude image is used as a training sample of a network, corresponding data amplification is carried out for the problem of less ice layer image data, and the wide applicability ofthe method is expanded. Lee filtering is carried out on the ice cover image. In order to save edge information as much as possible, a threshold judgment process is added to a filtering process. FCN-is constructed, and FCN-is constructed; according to the ASPP ice layer segmentation deep network, the ASPP layer is improved, so that the extraction capability of the network on small-scale characteristics is enhanced. The preliminary classification result is further processed through CRF, and the segmentation result is further refined on the basis of achieving end-to-end pixel level segmentation.In addition, the network greatly realizes the autonomous learning process.

The invention discloses a polarimetric SAR classification method on the basis of NSCT and discriminative dictionary learning and mainly solves the problems of low classification accuracy and low classification speed of an existing polarimetric SAR image classification method. The polarimetric SAR classification method comprises the following implementing steps: 1, acquiring a coherence matrix of a polarimetric SAR image to be classified and carrying out Lee filtering on the coherence matrix to obtain the de-noised coherence matrix; 2, carrying out Cloude decomposition on the de-noised coherence matrix and using three non-negative feature values of decomposition values and a scattering angle as classification features; 3, carrying out three-layer NSCT on the classification features and using a transformed low-frequency coefficient as a transform domain classification feature; 4, using the transform domain classification feature and combining a discriminative dictionary learning model to train a dictionary and a classifier; 5, using the dictionary and the classifier, which are obtained by training, to classify a test sample so as to obtain a classification result. The polarimetric SAR classification method improves classification accuracy and increases a classification speed and is suitable for image processing.

The invention discloses a selection method for counting an identical distribution space pixel based on a time sequence SAR (Synthetic Aperture Radar) image. The selection method comprises the following steps: firstly, carrying out pre-processing on an original SAR image data sequence to obtain a single-visual SAR strength image sequence; carrying out registration on the SAR strength image sequence; acquiring a rejection region of a likelihood ratio test under the assumption that a single-visual SAR strength image subjected to the registration complies with index statistical distribution; comparing the counting similarity of a time sample of each space pixel in a rectangular sliding window and a time sample of a central reference pixel; traversing each space pixel in a whole image and acquiring an identical statistical distribution sample of each space pixel; filtering the SAR image by adopting Lee filtering and the identical distribution sample of each space pixel. According to the selection method disclosed by the invention, samples with the same attribute of the reference pixel are selected through a likelihood ratio assumption test, so that a spatial distribution law and a backward scattering property of pixel points in a radar image are met better and a filtering image with the full resolution ratio which is closer to a real ground surface is obtained.

The invention discloses a polarized SAR image target detection method based on a deep stairway network, comprising the following steps: inputting a polarized SAR image to be detected and performing Lee filtering on the polarized coherent matrix T; solving the filtered T to obtain a polarized covariance matrix C; performing Yamahachi decomposition on the polarized covariance matrix C to form a pixel-based characteristics matrix F; normalizing the F and extracting blocks for each element of the normalized characteristics matrix F1 and listing them in a column; forming a characteristics matrix F2 based on the image blocks; obtaining a training set D according to F2; using the SLIC algorithm in the super pixels to obtain a test set T; constructing a target detection model based on a deep stairway network; using the training data set D to train the target detection model; and utilizing the trained target detection model to classify the test data set T. According to the invention, a deep stairway network is adopted, which only uses a small amount of labeled samples to obtain high target detection precision. The present invention can be used for the classification of ground targets.

The invention discloses a method for classifying polarimetric synthetic aperture radar (SAR) images on the basis of scattered power and intensity combined statistics. The method for classifying the polarimetric SAR images on the basis of the scattered power and the intensity combined statistics mainly solves the problem that in the prior art, zones with similar scattering properties are difficult to distinguish and classification numbers are fixed. The method is achieved by the following steps that: utilizing a Lee filter to conduct filtering on a coherence matrix T, utilizing Freeman decomposition to obtain a power matrix, utilizing eigenvalue decomposition to obtain an intensity matrix, conducting 8 neighborhood averaging on the power matrix and the intensity matrix respectively, selecting a k class homogeneous zone as a training sample, utilizing an EM algorithm to estimate parameters of probability density distribution functions of the power matrix and the intensity matrix of a k class sample, solving joint probability distribution of the power matrix and the intensity matrix of the k class sample, and conducting Bayesian classification on polarimetric SAR data to be classified to obtain classification results. The method for classifying the polarimetric SAR images on the basis of the scattered power and the intensity combined statistics has the advantages of being good in the classification effect of the polarimetric SAR images, and can be further used for target detection and target identification of the polarimetric SAR images.

The invention discloses a polarized SAR image classification method based on an extreme learning machine, mainly for solving the problem of low time efficiency of polarized SAR image classification. The method is realized by the following steps: inputting mark information of a polarized SAR image to be classified and a coherent matrix, and carrying out Lee filtering; extracting characteristics of the coherent matrix after the filtering, normalizing the characteristics to obtain a data set, and obtaining a mark set from the data set; dividing the mark set into a training set and a test set, and training the extreme learning machine by use of the training set; and predicating the type of the data set by use of the well trained extreme learning machine to obtain a polarized SAR image classification result. According to the invention, polarized SAR images are classified by use of the extreme learning machine, the classification precision is quite high, at the same time, the operation time is less, and the method can be applied to terrain classification and object identification.

The invention discloses a SAR image coastline extracting method based on a geometric active contour model. The method comprises steps of: reading a SAR image including a to-be-extracted coastline target by using Matlab software, filtering the SAR image including the to-be-extracted coastline target by using a Lee filter and adjusting the brightness of the SAR image; (2) performing convolution on the SAR image to generate a grid sampling point, drawing multiple small discs in the grid sampling point as the initial contour of the coastline; (3) using the initial contour of the coastline as an input of the geometric active contour model, vectorizing the coastline processed by the geometric active contour model by using area information combining images as a boundary stop condition of the geometric active contour model so as to finally obtain a continuous coastline. The method utilizes the global information of the images, has good expandability, and inhibits the influence of noise on coastline detection so as to accurately extract the coastline.

The invention discloses a polarized SAR (Specific Absorption Rate) image classifying method based on super-vector coding. The method comprises the following realizing steps: (1) inputting images; (2) refining Lee filter; (3) extracting a scattering characteristic vector; (4) coding super-sparsely; (5) obtaining super-vector characteristics; (6) normalizing and expanding the sample characteristic set; (7) selecting a training sample and a test sample; (8) training the classifier to classify the images; (9) calculating the classifying precision; (10) outputting a result. According to the method provided by the invention, the image characteristics extracted are unlikely to be affected by noise points, and are small in redundancy and good in representability, thereby being applicable to effectively improving the classification precision in the course of classifying, and detecting and identifying an SAR image objective of a polarized and synthetic aperture radar.

The invention provides an image tracking method based on image frequency domain conversion. The method comprises the following steps: firstly, preprocessing input SAR image data; on the basis of a traditional delicate Lee filter, introducing a window size adaptive selection method based on equivalent opinions, and forming a filtering algorithm adaptive to the window size; secondly, establishing anSAR image dense sample application feature extraction algorithm based on a two-dimensional sinc function model; developing a multi-layer multi-window matching strategy, and determining the reliable matching window size in a self-adaptive mode according to the error measurement output by matching of different layers; developing a sub-pixel phase correlation algorithm based on multi-scale phase consistency characteristics, constructing multi-scale phase consistency structure images, and estimating the phase difference between the images in a frequency domain space. Through the verification of the specific embodiment, the tracking method provided by the invention can effectively track the target feature image, and improves the accuracy and robustness.

The invention discloses a full-polarization SAR image speckle suppression method. Polarization decomposition is carried out on a target by combining a polarization SAR scattering model on the basis ofnon-local Lee filtering, a more accurate similar set is obtained according to polarization similarity among pixels, and scene target polarization scattering characteristics and texture detail information are better kept while noise is filtered, so that a visualization result of coherent speckle suppression is obtained; in this way, the problems that an existing algorithm is high in complexity andtoo long in consumed time are solved through the improved weight acceleration algorithm, then, an image accumulation matrix is introduced to eliminate redundant calculation, the calculation complexity is greatly reduced on the basis that the filtering effect of the algorithm is kept, and the operation efficiency is remarkably improved.

The invention discloses novel tilapia feed produced from cassava lees through microbial fermentation and a preparation method of the tilapia feed and belongs to the technical field of tilapia feed processing. The feed is prepared from raw materials in parts by weight as follows: 75-93 parts of dry cassava lees filter cakes, 18-35 parts of pure insect pulp albumen powder, 12-18 parts of earthworm powder, 22-36 parts of soybean meal, 17-24 parts of peanut meal, 2.5-4.5 parts of a phagostimulant and 3.2-5.8 parts of a traditional Chinese medicine preparation through drying, crushing, sieving, mixing, granulation, drying, sterilization and other steps. The feed integrates efficacy of multiple traditional Chinese medicines and can have functions of resisting bacteria, diminishing inflammation, clearing heat, removing toxicity, enhancing the immunity of a body and the like. The feed is well absorbed by tilapia, the death rate of the tilapia suffering from streptococcicosis can be effectively decreased, and the culture risk is reduced. The feed is reasonable to prepare, contains rich nutrients and can increase the feed intake of the tilapia suffering from streptococcicosis, increase the growth speed and shorten the growth cycle.