481 results about "Polarimetric sar" patented technology

The invention discloses a polarimetric SAR (Synthetic Aperture Radar) image classification method based on an SDIT (Secretome-Derived Isotopic Tag) and an SVM (Support Vector Machine). The method comprises the implementation steps of (1) inputting an image, (2) filtering, (3) extracting scattering and polarization textural features, (4) combining and normalizing the features, (5) training a classifier, (6) predicting classification, (7) calculating precision and (8) outputting a result. Compared with an existing method, the polarimetric SAR image classification method based on the SDIT and the SVM enables the empirical risk and the expected risk to be minimal at the same time, and has the advantages of high generalization capability and low classification complexity and also the advantages of describing the image characteristics comprehensively and meticulously and improving the classification precision, and in the meantime, the polarimetric SAR image classification method has a good denoising effect, and further is capable of enabling the outlines and edges of the polarimetric SAR images to be clear, improving the image quality, and enhancing the polarimetric SAR image classification performance.

This invention relates to improved ultra-wideband synthetic aperture radar and inverse synthetic aperture radar, capable of simultaneously and independently imaging a plurality of spectral and polarimetric channels covering multiple radio frequency octaves. Advances in technologies relating to signal processing, graphical user interfaces, color representations of multi-spectral radar images, low aerodynamic drag polarimetric SAR antenna systems, and synthetic aperture radar aircraft platforms are some of the advancements disclosed herein.

The invention discloses a polarization SAR image classification method based on a deep neural network. The method mainly solves the problems that traditional polarization SAR image classification accuracy is low and boundaries are disorderly. The method includes the classification steps that a power diagram I is acquired from polarization SAR data through Pauli decomposition, the power diagram I is segmented in advance, and then a plurality of small blocks are acquired; a training data set U is selected from a polarization SAR image, input into a two-layer self-coding structure for training and then classified through a Softmax classifier; a test data set V is selected from the polarization SAR image and input into the trained two-layer self-coding structure, and then classification labels are acquired through the Softmax classifier; in the pre-segmented small blocks, the classification labels and channel information of the power diagram I are combined, and then small block labels are acquired. The polarization SAR image classification method has the advantages that the recognition rate is high, result region consistency is good, and the method can be used for polarization SAR homogeneous region terrain classification.

The invention discloses a small-sample polarized SAR ground feature classification method based on a deep convolutional twin network, and mainly solves a problem that a conventional method is low in classification precision because the number of polarized SAR data mark samples is smaller. The method of the invention comprises the steps: 1), inputting a to-be-classified polarized SAR image and a real ground object mark of the to-be-classified polarized SAR image, and carrying out the Lee filtering; 2), extracting an input feature vector from the filtered to-be-classified polarized SAR data, andcarrying out the dividing of a training sample set and a test sample set; 3), carrying out the combination of each two samples in the training sample set, and obtaining a sample pair training set; 4), building the deep convolutional twin network, and carrying out the training of the deep convolutional twin network through the training sample set and the sample pair training set; 5), carrying outthe classification of the samples in the test set through the trained deep convolutional twin network, and obtaining the classes of ground features. According to the invention, the method expands thetraining set under the twin configuration, achieves the extraction of the difference features, enables the classification precision of a model to be higher, and can be used for the target classification, detection and recognition of a polarized SAR image.

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 a polarized SAR (synthetic aperture radar) image classification method based on a depth PCA (principal component analysis) network and an SVM (support vector machine) classifier. The polarized SAR image classification method includes filtering a polarized SAR image, extracting a shape feature parameter, a scattering feature parameter, a polarization feature parameter and independent elements of a covariance matrix C, and combing and normalizing into new high-dimensional features serving as data to be processed in a next step; according to actual ground feature flags, randomly selecting 10% of data with flags from each type to serve as training samples; whitening the training samples to serve as input to train a first layer of the network, taking a result as input of a second layer to train the second layer of the network, and performing binaryzation and histogram statistics on an output result; taking output of the depth PCA network as a finally learned feature training SVM classifier; whitening test samples, and inputting the test samples into a trained network framework to predict and calculate accuracy; coloring and displaying a classified image and outputting a final result.

The invention discloses a generative and adversarial network-based polarimetric SAR image classification method, and mainly solves the problem of low classification precision caused by unreasonable selection of polarimetric SAR image features in the prior art. Unlabeled and labeled samples are fully utilized. A complex cost function does not need to be designed. The method comprises the followingimplementation steps of (1) inputting a coherence matrix of to-be-classified polarimetric SAR images; (2) performing filtration; (3) generating a sample set; (4) selecting samples; (5) constructing generative and adversarial networks; (6) training the generative and adversarial networks; (7) classifying the sample set; and (8) outputting category labels of all the samples in the sample set of thepolarimetric SAR images. The method has the advantage of remarkable polarimetric SAR image classification effect, and can be further used for target detection and target identification of the polarimetric SAR images.

To provide a synthetic aperture radar for achieving a compact polarimetric SAR easily by using a general-purpose phased array antenna for vertical and horizontal polarizations. An antenna section is a phased array antenna for vertical and horizontal polarizations capable of switching to the vertical or horizontal polarizations in transmission at every transmission / reception module, and receiving two of the horizontal and vertical polarizations simultaneously. The control system divides electrically the phased array antenna in the elevation direction in transmission to set one of them for horizontal polarization transmission and the other for vertical polarization transmission, and sets the antenna for dual polarization simultaneous reception to receive the horizontal and vertical polarizations. The SAR processor takes complex data of horizontal and vertical polarization receiving data as a target vector, and obtains a calculation result corresponding to a covariance matrix of a target vector in full polarimetry so as to perform polarimetric SAR processing.

The invention discloses a polarized SAR image classification method based on a nonsubsampled contourlet convolutional neural network, and mainly at solving the problems that influence of speckle noises is hard to avoid and the classification precision is low in the prior art. The method comprises the steps that a polarized SAR image to be classified is denoised; Pauli decomposition is carried out on a polarized scattering matrix S obtained by denoising; image characteristics obtained via Pauli decomposition are combined into a characteristic matrix F, and the characteristic matrix F is normalized and recorded as F1; 22*22 blocks surrounding the F1 are taken for each pixel point to obtain a block based characteristic matrix F2; a training data set and a test data set are selected from the F2; the nonsubsampled contourlet convolutional neural network is established to train the training data set; and the trained nonsubsampled contourlet convolutional neural network is used to classify the test data set. The polarized SAR image classification method improves the expression capability and the classification precision of the features of the polarized SAR image, and can be used for target identification.

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 radar remote sensing-based detection method of soil alkalization, comprising the following steps: computing the polarization parameter based on full polarimetric SAR data, dividing bare areas and vegetation-covered areas and generating images with masks; delimiting test areas and adopting measuring data to establish a dielectric model of moist salt soil; adopting the full polarimetric SAR data to invert a complex permittivity map and carrying out RD geocoding, data information separation and conversion; based on the dielectric model and the complex permittivity, adopting the genetic algorithm for inversion to obtain moisture contents and salt contents of areas, and integrating geographic coordinate information and a vegetation cover mask map to generate a moisture content map and a salt content map; adopting the complex permittivity and the moisture contents of areas to differentiate saline soil from alkali soil. By adopting the technical proposal of the invention, the moisture content and the salt content of the soil in saline areas can be obtained, the degree of dependence on ground assistant data is reduced and simultaneously the dielectric behavior of the moist salt soil can be better depicted.

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.

The invention discloses a polarization SAR data classification method and system based on a mixed classifier. The polarization SAR data classification method includes the steps of obtaining polarization SAR data initial polarization characteristics in different classes, adopting a decision tree classifier to select the polarization characteristics for classification from the initial polarization characteristics, being based on the polarization characteristics for classification, and adopting an SVM classifier to classify polarization SAR data. According to the polarization SAR data classification method and system, the advantages of the decision tree classifier and the advantages of the SVM classifier are synthesized, the classification accuracy reaches the level of the SVM classifier, the classification calculation efficiency is equivalent to that of the decision tree classifier, and the polarization SAR data classification method and system are of great significances in polarization SAR data classification.

The invention discloses a method for classifying polarimetric SAR (synthetic aperture radar) images on the basis of Cloude decomposition and K-wishart distribution and mainly solves the problem that the prior art is poor in classification effect and high in computation complexity. The method includes the steps of firstly, reading a polarimetric SAR image to be classified, subjecting each pixel of the image to Cloude decomposition to obtain an entropy H and a scattering angle alpha; secondly, initially partitioning the polarimetric SAR image according to the values of the entropy H and the scattering angle into eight classes; thirdly, subjecting results of eight classes of the whole polarimetric SAR image to iteration to obtain more accurate classification results. Compared with classic classification methods, the method is stricter in partitioning the polarimetric SAR image, better in classification effect, lower in computation complexity and applicable to terrain classification and target recognition of the polarimetric SAR images.

The present invention discloses a polarized SAR image classification method based on the semi-supervised depth distance metric network, and the technical problems that the traditional depth learning only considers the non-linear relationship between the sample characteristics and the classification accuracy is not high when the number of marked samples is relatively small are solved. The method comprises the following steps: inputting to-be-classified polarized SAR image data; solving a neighboring sample of the marked sample; constructing the loss function of the semi-supervised large boundary neighbor algorithm; initializing parameters of the network; pre-training the network; carrying out fine tuning on the network; carrying out classification prediction on the unmarked samples; and outputting a classification result image and classification accuracy of the to-be-classified polarized SAR image. According to the method disclosed by the present invention, by constructing a depth distance metric network, a popular learning regular term is added to the large boundary neighbor algorithm, so that problems of the influence of insufficient marked samples on the classification accuracy and the waste of information of a large number of unmarked samples are overcome; and the characteristics learned in the method of the present invention fully depicts intrinsic attributes of the samples, and the method can be applied to the earth resources survey, military systems and other technical fields.

The invention discloses a polarized SAR image classifying method based on a sparse automatic encoder. The problem that extraction of polarized SAR image features is influenced by data irrelevance and redundancy, accordingly the classifying process is complicated and unreasionabl feature selection causes low classifying accuracy is mainly solved. The polarized SAR image classifying method based on the sparse automatic encoder comprises the specific steps of 1 inputting coherence matrixes of polarized SAR images to be classified; 2 perform filtering, 3 selecting samples; 4 obtaining sparse principal components; 5 training the sparse automatic encoder; 6 extracting the features; 7 performing classification through a support vector machine; 8 outputting a classification result. The polarized SAR image classifying method has the advantage of having remarkable polarized SAR image classifying effect and can be further used for target detection and target recognition of the polarized SAR images.

The invention provides a multi-polarization SAR compact polarimetry calibration method which comprises a calibration field compact polarimetry SAR data preprocessing process and a polarization distortion parameter calibration process. In the calibration field compact polarimetry SAR data preprocessing process, compact polarimetry SAR data radiation correction and Faraday rotation correction are mainly completed. In the polarization distortion parameter calibration process, four different types of common corner reflectors are used as reference calibrators, and a compact polarimetry calibration method (namely, a compact polarimetry polarization distortion parameter calibration method) is deduced based on a calibration model. Through the method, a polarization distortion parameter can be calibrated precisely. The calibration algorithm does not depend on the absolute RCS values of the corner reflectors, and calibration error caused by RCS inconsistency of different corner reflectors can be avoided effectively. The algorithm is of high robustness and practicability.

The invention discloses a polarized SAR land feature classification method based on a full convolution neural network, comprising: performing Pauli decomposition on a to-be-classified polarized scattering matrix S to obtain the odd scattering coefficient, the even scattering coefficient and the volume scattering coefficient; using the odd scattering coefficient, the even scatter coefficient and the volume scattering coefficient as the three-dimensional image characteristics F of the polarized SAR image; then converting the obtained three-dimensional image characteristics matrix F into an RGB image F1; randomly selecting m x n pixel blocks in the RGB image F1 as training samples; using the whole RGB image F1 as a testing sample; re-constructing a full convolution neural network model; training the training samples through the full convolution neural network to obtain a trained model; and then, through the trained model, classifying the test set and obtaining the classification result. The method of the invention can solve the problem of low time efficiency in the prior art and shorten the running time under the condition of high classification accuracy.

The present invention discloses a polarization SAR ground object classification method based on a self-step learning convolutional neural network, in order to mainly solve the problems that the priorart has low accuracy in classifying complex ground object scenes and is heavily affected by noise. The implementation scheme comprises: 1, obtaining a polarization scattering matrix S and a pseudo color RGB image under the Pauli basis from original complete polarization SAR data; 2, constructing a three-dimensional matrix to form a sample set for each pixel, and constructing a training sample setand a test sample set; 3, constructing a convolutional neural network and training the convolutional neural network based on self-step learning to accelerate network convergence and improve the generalization ability of the network; and 4, classifying the test samples by using the trained convolutional neural network to obtain a final complete polarization SAR ground object classification result.According to the method disclosed by the present invention, accuracy for classifying the target ground objects of complex ground object scenes in the polarization SAR image is improved, and the methodcan be used for feature classification and target recognition.

The invention discloses a polarized SAR (Synthetic Aperture Radar) image semi-supervised classification method capable of considering characteristic optimization. The method comprises the following steps: firstly, adopting a refined polarized LEE filtering method to carry out filtering, extracting polarization characteristics, carrying out combination to obtain an original characteristic set, and carrying out normalization processing; selecting an initial training sample set and a no-label set, and carrying out characteristic selection and classifier parameter optimization through a hybrid coding genetic algorithm under the initial training sample set; reconstructing the training sample set and a no-label sample set; training the classifier, and selecting a candidate set from the no-label sample set; utilizing a trained SVM (Support Vector Machine) classifier to label the candidate set, and selecting and expanding sample points with a high confidence coefficient into the training sample set; repeating the training of the classifier until learning is finished; and classifying the whole image by a finally trained SVM to obtain a classification thematic map. By use of the classification method, on one hand, effective characteristics can be adaptively extracted to improve a semi-supervised classification effect; and on the other hand, the efficiency of self-training learning can be improved, and error accumulation can be effectively avoided.

The invention discloses a super-pixel polarimetric SAR land feature classification method based on sparse representation. The method comprises: inputting polarimetric SAR image data to be classified, processing the image, and thereby obtaining a pseudocolor image corresponding to Pauli decomposition; performing super-pixel image over-segmentation on the pseudocolor image to obtain a plurality of super-pixels; extracting features, which are seven-dimensional, of radiation mechanism of the original polarimetric SAR image as features of every pixel; performing super-pixel united sparse representation to obtain sparse representation of each super-pixel feature; classifying by using a sparse representation classifier; working out the mean value of each super-pixel covariance matrix, then performing super-pixel complex Wishart iteration by using the classifying result in the last step, and at last obtaining a final classifying result. According to the super-pixel polarimetric SAR land feature classification method based on sparse representation, the problem that traditional classifying areas based on the single pixel are poor in consistency is solved, and operating speed of the algorithm is greatly increased on basis of improving accelerate.

The invention relates to a polarization SAR (Synthetic Aperture Radar) terrain radiation correction and geometric correction method based on imaging surface representation. According to the method, the radiation value of the imaging surface of an SAR image is taken as the representation of a backscattering coefficient. The method comprises the following steps of 1, obtaining the SAR image and a radiation calibration file external DEM (Dynamic Effect Model) in a region; 2, performing radiation calibration according to a radiation calibration file of the original polarization SAR image; 3, calculating the unit area of the external DEM; 4, generating an SAR image line and column number lookup table according to a distance Doppler SAR positioning model and calculating the projection area of an equiphase surface; 5, generating an SAR simulation image by combining the projection area of the equiphase surface with the line and column number lookup table; 6, matching a real SAR image with the simulation SAR image, building a polynomial correction equation, and refining the line and column number lookup table; 7, carrying out terrain radiation correction on the polarization SAR image according to the backscattering coefficient expression based on the imaging surface of the SAR image; 8, carrying out geometric correction according to the refined line and column number lookup table. According to the scheme provided by the invention, the radiation distortion of the polarization SAR image, caused by terrain can be corrected, and the high-precision geometric positioning and correction of the polarization SAR image are realized.

The invention aims to provide a polarization SAR image classification method based on an SSAE and an FSALS-SVM. According to the polarization SAR image classification method, a multi-implicit-strata structure of the stacked sparse automatic encoder (SSAE) is used for obtaining depth features which have the capacity for describing original data more intrinsically and are more suitable for classification, the fast sparse approximation least square-support vector machine (FSALS-SVM) which can obtain sparse solutions is used for replacing the Softmax commonly used in traditional deep learning and being combined with the SSAE, the classification accuracy of polarization SAR images is improved, the defect that a traditional polarization SAR image classification method based on pixels is greatly affected by speckle noise is overcome to a certain degree, and therefore coherence of homogeneous areas in classification result images is ensured.

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 discloses a polarized SAR (Synthetic Aperture Radar) image classifying method. The polarized SAR image classifying method comprises the following steps: S1, extracting characteristics of a polarized SAR image as follows: scattering entropy H, anti-entropy A and a scattering angle alpha, wherein an obtained characteristic set (H, A, alpha) is taken as a first characteristic set; S2, after decomposing the polarized SAR image into two sub-aperture images, respectively extracting characteristics of the two sub-aperture images as follows: scattering entropies H, anti-entropies A and scattering angles alpha, thus obtaining two sub characteristic sets (H1, A1, alpha 1) and (H2, A2, alpha 2); S3, subtracting each corresponding characteristic value in the two sub characteristic sets to obtain a set (Delta H, Delta A, Delta alpha) of a difference value of each corresponding characteristic as a second characteristic set; and S4, inputting the first characteristic set and the second characteristic set into a decision-making tree classifying model to obtain the classified result of the polarized SAR image. The polarized SAR image classifying method disclosed by the invention is used, so that precision of classified result can be improved.

The invention discloses a sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification method. The implementation of the sparse depth network based polarization SAR image classification method comprises step 1, inputting an image; step 2, performing filtering; step 3, extracting features; step 4, constructing and training a sparse depth network; step 5, performing prediction and classification; step 6, outputting a result. Compared with the existing method, an energy function can be minimized, a distortion function can be controlled within a certain range to enable a hidden layer to be sparse, and accordingly the calculation is greatly reduced, the generalization capability is high, and the classification accuracy is high.

The invention provides a method for measuring tree heights by a tomography synthetic aperture radar (SAR) based on compression multi-signal classification (CS-MUSIC). The method solves the problem that the aerial photography frequency is high in traditional SAR tomography, an echo observation vector matrix is established by using the correlation among all polarization channel signals of the complete polarization SAR, and the CS-MUSIC algorithm is used for conducting elevation scattering coefficient reconstruction. Compared with a traditional spectrum estimation SAR tomography method, the number of false targets can be decreased in the method, and meanwhile the aerial photography frequency needed by the tomography SAR in the tree height measuring process is lowered.

The invention provides a sea surface wind field inversion method and device and belongs to the technical field of a synthetic aperture radar. The method comprises steps that a cross polarization synthetic aperture radar (SAR) remote sensing image and European medium and long term weather forecast center (ECNWF) re-analysis wind field data are acquired; according to the acquired cross polarizationSAR remote sensing image and the ECNWF re-analysis wind field data, the corresponding relationship between a normalization backward scattering cross section value NRCS of the cross polarization SAR remote sensing image based on a radar incidence angle and the wind speed of the ECNWF re-analysis wind field data is acquired; sea surface wind field inversion is acquired according to the correspondingrelationship. The method is advantaged in that sea surface wind field inversion can be carried out without the auxiliary wind direction information, and sea surface wind field inversion of the synthetic aperture radar remote sensing image based on the cross polarization mode is realized.