CN112884007A - SAR image classification method for pixel-level statistical description learning - Google Patents
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Abstract
The invention discloses an SAR image classification method for pixel-level statistical description learning, which comprises the following steps: s1, inputting the target SAR image into the SAR image classification model; s2, extracting the pixel-level statistical description characteristics of the target SAR image by a discrimination sub-network in the SAR image classification model; s3, extracting structural mode description features of the target SAR image by a mode sub-network in the SAR image classification model; s4, fusing the pixel-level statistical description features and the structural mode description features by a fusion module in the SAR image classification model to obtain image description features of the target SAR image; and S5, generating a classification result of the target SAR image based on the image description characteristics by a Softmax layer in the SAR image classification model. The SAR image analysis method based on the fuzzy clustering can solve the problems of low generalization capability and insufficient robustness in SAR image analysis.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an SAR image classification method for pixel-level statistical description learning.
Background
Synthetic Aperture Radar (SAR) is a research direction for earlier development in the technical field of modern radar signal processing, is an active microwave remote sensing imaging system, and is widely applied to the military and civil fields. The SAR system can simultaneously obtain a clutter signal of the surface feature including amplitude and phase information, which carries rich target information. The SAR image interpretation technology is an important means for analyzing SAR image information, and with the continuous development of SAR imaging systems and the improvement of data acquisition capacity, the analysis and interpretation of massive SAR data become research hotspots.
The traditional SAR image classification method comprises image statistical modeling, texture analysis and other methods. The image statistical modeling method generally realizes SAR image classification based on a Bayesian decision theory framework, and typically comprises the following steps: weibull distribution, logarithmic gaussian distribution, and K distribution, among others. However, due to its limited degrees of freedom, this type of distribution still has deficiencies in fitting non-gaussian distributed SAR data. In the texture analysis method, image classification is realized by extracting texture features of the SAR image, and in the research of the method, the features obtained based on the traditional texture analysis method are usually features with low-level or middle-level semantics, and the image description capability is limited.
In recent years, deep learning has become the mainstream of development of image processing. Convolutional Neural Network (CNN) is one of the most popular deep learning models at present, and omits a process of artificially selecting features, and instead, automatically extracts image features for tasks such as classification, semantic segmentation, target detection and the like through multilayer processing primitives, such as processing primitives of convolution, nonlinear activation, pooling and the like. By means of the characteristic parameters with extremely strong expression capability, the convolutional neural network makes breakthrough development on the image classification problem. However, the method does not consider the pixel level statistical characteristics and the high-level structure semantic characteristics of the SAR image at the same time, has low universality in the SAR image analysis field, is difficult to process the problems of granular noise, image distortion and the like caused by a coherent imaging mechanism, and further has an unsatisfactory classification effect.
In summary, a new technical solution is urgently needed to solve the problems of low generalization capability and insufficient robustness in the SAR image analysis.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses an SAR image classification method based on pixel-level statistical description learning, which is used for solving the problems of low generalization capability and insufficient robustness in SAR image analysis, effectively solving the problems of granular noise and image distortion caused by a coherent imaging mechanism and improving the classification effect.
In order to solve the technical problems, the invention adopts the following technical scheme:
a SAR image classification method of pixel level statistical description learning comprises the following steps:
s1, inputting the target SAR image into the SAR image classification model;
s2, extracting the pixel-level statistical description feature z of the target SAR image by a discrimination sub-network in the SAR image classification modelps；
S3, extracting structural mode description characteristic z of target SAR image by mode sub-network in SAR image classification modelpa；
S4, a fusion module in the SAR image classification model describes the characteristic z with the pixel level statisticspsAnd structural Pattern description feature zpaFusing to obtain an image description characteristic z of the target SAR image;
and S5, generating a classification result of the target SAR image based on the image description characteristics by a Softmax layer in the SAR image classification model.
Preferably, step S2 includes:
s201, extracting a pixel value mean value mu of the target SAR image based on the following formulaxSum pixel value standard deviation σx：
In the formula, xiExpressing the pixel value of the ith pixel point of the target SAR image, and expressing the number of the pixel points of the target SAR image by n;
s202, based on the following formula, the method is used for muxAnd σxCarrying out scale and translation transformation to obtain corresponding characteristics zμAnd zσ：
zμ＝wμμx+bμ
zσ＝wσσx+bσ
In the formula, zμ∈V，zσE is V, and V is a high-dimensional mapping space; w is aμAnd bμRespectively represent muxCorresponding scale and translation transformation vectors, wμ＝[wμ1,wμ2,...,wμD]T，bμ＝[bμ1,bμ2,...,bμD]T，bμdAnd wμdRespectively represent muxD-th value of (D) corresponds to the scale and translation transformation parameter, D-1, 2, …, D denotes μxThe maximum dimension of; w is aσAnd bσRespectively represent sigmaxCorresponding scale and translation transformation parameters, wσ＝[wσ1,wσ2,...,wσD]T，bσ＝[bσ1,bσ2,...,bσD]T，bσdAnd wσdRespectively represent sigmaxThe scale and translation transformation parameters corresponding to the d-th value of (a);
s203, pair z based on the following formulaμAnd zσAdaptive optimization and nonlinear processing are carried out to generate pixel-level statistical description characteristics zps：
In the formula (I), the compound is shown in the specification,
ReLU (-) is a modified linear activation unit function,
and
respectively represents zpsThe corresponding weight matrix and the bias vector,
representing an M-dimensional linear space of,
representing an M x 2D dimensional linear space.
Preferably, the mode subnetwork includes 4 convolutional layers with all the activation functions as ReLU functions, and the processing formula of the kth convolutional layer is:
zk＝H(Wkzk-1+bk)
in the formula, zkDenotes the output of the kth convolutional layer, k is 1,2,3,4, z0For the target SAR image, H (-) denotes a composite function of the ReLU activation mapping and pooling functions, WkAnd bkRespectively represents zkThe corresponding weight matrix and the bias vector,
zpafeatures are described for the structural modes.
Preferably, in step S4, the feature z is statistically described at the pixel level based on the following equationpsAnd structural Pattern description feature zpaAnd (3) fusing to obtain an image description characteristic z of the target SAR image:
z＝ReLU(Wpszps+Wpazpa)
wherein ReLU (. cndot.) is a modified linear activation unit function, WpsAnd WpaAre each zpsAnd zpaAnd (4) corresponding weight matrix.
Preferably, the method further comprises the following steps:
and S6, after the SAR image classification model training is finished each time, optimizing the parameters of the SAR image classification model by using a random gradient descent algorithm, wherein the optimization target is to find the minimum average loss function.
Preferably, in step S6, the parameter optimization of the pattern sub-network is performed based on the following formula:
in the formula, Xn'Denotes the nth' sample, yn'And
are each Xn'The true tag and the estimated tag of (a),
in order to be a function of the loss,
in the formula (I), the compound is shown in the specification,<·>and ln (·) respectively represent the inner product sum logarithm operation, Yn'Representing a genuine label yn'Label vector based on One-Hot coding, an'Represents Xn'The output vector after passing through the Softmax layer, the jth output of the Softmax layer is
Comprises the following steps:
in the formula, zj is the jth input of the Softmax layer, and M represents the number of the input of the Softmax layer;
the update formula of the weight parameter of the k-th layer convolution in the pattern sub-network is as follows:
in the formula (I), the compound is shown in the specification,
a weight parameter representing the k-th layer convolution after the i +1 th layer is suboptimal,
a weight parameter representing the ith sub-optimized k-th layer convolution,
the gradient of the weight parameter of the loss function relative to the k-th layer convolution is shown, M represents the number of training samples, and eta represents the learning rate.
In summary, the invention discloses an SAR image classification method for pixel-level statistical description learning, which combines the statistical characteristics of an SAR image with the learning capability of a convolutional neural network. Based on a statistical description learning theory, a discriminant sub-network is utilized to extract pixel-level first-order and high-order statistical primitives of the SAR image, and discriminant pixel-level statistical description is learned through high-dimensional mapping of the network under the constraint of minimized classification errors. In addition, the structural mode description of the SAR image is hierarchically learned by utilizing a multilayer network structure, and the hierarchical relevance between local pixel points of the SAR image is extracted. And finally, under the constraint of minimized classification errors, carrying out joint optimization on the pixel-level statistical description and the structural mode description to generate the final description characteristics of the SAR image. Compared with the prior art, the SAR image classification method based on the fuzzy clustering can solve the problems of low generalization capability and insufficient robustness in SAR image analysis, effectively process granular noise and image distortion caused by a coherent imaging mechanism, and improve the classification effect
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For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
FIG. 1 is a schematic diagram illustrating the principle of a SAR image classification method by pixel level statistics description learning disclosed in the present invention.
Fig. 2 is a schematic diagram of a SAR image classification model in the present invention.
FIG. 3 is a diagram illustrating the extraction of image mean and standard deviation statistical primitives in the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention discloses a method for classifying an SAR image through pixel-level statistical description learning, comprising the following steps:
s1, inputting the target SAR image into the SAR image classification model;
s2, extracting the pixel-level statistical description feature z of the target SAR image by a discrimination sub-network in the SAR image classification modelps；
S3, extracting structural mode description characteristic z of target SAR image by mode sub-network in SAR image classification modelpa；
S4, a fusion module in the SAR image classification model describes the characteristic z with the pixel level statisticspsAnd structural Pattern description feature zpaFusing to obtain an image description characteristic z of the target SAR image;
and S5, generating a classification result of the target SAR image based on the image description characteristics by a Softmax layer in the SAR image classification model.
The method in the prior art is difficult to process granular noise points, image distortion and a complex scattering mechanism of the target caused by a coherent imaging mechanism, so that the classification effect is not ideal. The generalization capability mentioned in the invention is not high, and the pointer needs to adopt different training models for different types of SAR pictures. The SAR image classification method of pixel level statistical description learning disclosed by the invention is based on the statistical description learning theory, establishes a unified model of SAR image bottom layer statistical description learning and high-layer structure mode description learning, enhances the effective description of SAR image modes with random characteristics and structural characteristics, not only improves the classification precision, but also improves the universality of the network
In specific implementation, step S2 includes:
s201, extracting a pixel value mean value mu of the target SAR image based on the following formulaxAnd imageStandard deviation of prime value sigmax：
In the formula, xiExpressing the pixel value of the ith pixel point of the target SAR image, and expressing the number of the pixel points of the target SAR image by n;
s202, based on the following formula, the method is used for muxAnd σxCarrying out scale and translation transformation to obtain corresponding characteristics zμAnd zσ：
zμ＝wμμx+bμ
zσ＝wσσx+bσ
In the formula, zμ∈V，zσE is V, and V is a high-dimensional mapping space; w is aμAnd bμRespectively represent muxCorresponding scale and translation transformation vectors, wμ＝[wμ1,wμ2,...,wμD]T，bμ＝[bμ1,bμ2,...,bμD]T，bμdAnd wμdRespectively represent muxD-th value of (D) corresponds to the scale and translation transformation parameter, D-1, 2, …, D denotes μxThe maximum dimension of; w is aσAnd bσRespectively represent sigmaxCorresponding scale and translation transformation parameters, wσ＝[wσ1,wσ2,...,wσD]T，bσ＝[bσ1,bσ2,...,bσD]T，bσdAnd wσdRespectively represent sigmaxThe scale and translation transformation parameters corresponding to the d-th value of (a);
s203, pair z based on the following formulaμAnd zσAdaptive optimization and nonlinear processing are carried out to generate pixel-level statistical description characteristics zps：
In the formula (I), the compound is shown in the specification,
ReLU (-) is a modified linear activation unit function,
and
respectively represents zpsThe corresponding weight matrix and the bias vector,
representing an M-dimensional linear space of,
representing an M x 2D dimensional linear space.
Firstly, extracting first-order and high-order statistical primitives of an input SAR image, wherein the formula is realized as shown in FIG. 3, and the method specifically comprises the following steps:
1) performing single-scale average pooling processing on the input image by a spatial pyramid pooling module (SPP) to obtain a pixel value average value mu of the target SAR imagex；
2) The pixel value standard deviation sigma can be obtained by combining the SPP module, the Power module and the Eltwise modulex。
After extracting the mean and standard deviation statistical primitives, the DiscNet maps the statistical primitives to a high-dimensional space through autonomous scale and translation transformation. The corresponding high-dimensional characteristics of the mean value and the standard deviation after the high-dimensional mapping are respectively recorded as zλE.g. V and zμAnd e.g. V, wherein V is a high-dimensional mapping space. After high-dimensional mapping, the z pair is constrained by using the minimized classification errorμAnd zλThe interaction between the two is carried out with self-adaptive optimization and nonlinear processing, and finally, pixel-level statistical description characteristics are generated.
The discrimination sub-network is used for extracting pixel-level statistical elements of the input SAR image and generating the SAR image bottom-layer pixel-level statistical description with discrimination through nonlinear and linear transformation. The pixel-level statistical description is learned through linear and nonlinear high-dimensional mapping after the average value statistical primitive and the standard deviation statistical primitive of the SAR image are extracted by utilizing the discrimination subnetwork, the effect is better than that of directly describing the pixel-level statistical characteristics of the SAR image by using the average value statistical primitive and the standard deviation statistical primitive, and the accuracy of the finally trained model is higher.
In specific implementation, the mode subnetwork includes 4 convolutional layers with all the activation functions as ReLU functions, and the processing formula of the kth convolutional layer is as follows:
zk＝H(Wkzk-1+bk)
in the formula, zkDenotes the output of the kth convolutional layer, k is 1,2,3,4, z0For the target SAR image, H (-) denotes a composite function of the ReLU activation mapping and pooling functions, WkAnd bkRespectively represents zkThe corresponding weight matrix and the bias vector,
zpafeatures are described for the structural modes.
In the present invention, as shown in fig. 2, the mode subnetwork includes 4 convolutional layers, where the convolutional kernel sizes of the first, second, and third convolutional layers are all 3 × 3, the step size is all 1, and the activation functions are all ReLU functions. The fourth convolutional kernel is 1 × 1, the step size is 1, and the activation function is the ReLU function.
In particular, in step S4, the pixel level statistics are described as zpsAnd structural Pattern description feature zpaAnd (3) fusing to obtain an image description characteristic z of the target SAR image:
z＝ReLU(Wpszps+Wpazpa)
wherein ReLU (. cndot.) is a modified linear activation unit function, WpsAnd WpaAre each zpsAnd zpaAnd (4) corresponding weight matrix.
When the concrete implementation, still include:
and S6, after each network training is finished, optimizing the parameters of the whole network by using a random gradient descent algorithm, wherein the optimization aim is to find the minimum average loss function.
In step S6, in a specific implementation, the parameters of the pattern sub-network are optimized based on the following formula:
in the formula, Xn'Denotes the nth' sample, yn'And
are each Xn'The true tag and the estimated tag of (a),
in order to be a function of the loss,
in the formula (I), the compound is shown in the specification,<·>and ln (·) respectively represent the inner product sum logarithm operation, Yn'Representing a genuine label yn'Label vector based on One-Hot coding, an'Represents Xn'Output vector after passing through Softmax layer, jth output of Softmax layer
(n' th sample X)n'What is output is a set of numbers,
j, representing this set of numbers) is:
in the formula, zjThe j input of the Softmax layer, wherein M represents the input number of the Softmax layer;
the update formula of the weight parameter of the k-th layer convolution in the pattern sub-network is as follows:
in the formula (I), the compound is shown in the specification,
a weight parameter representing the k-th layer convolution after the i +1 th layer is suboptimal,
a weight parameter representing the ith sub-optimized k-th layer convolution,
the gradient of the weight parameter of the loss function relative to the k-th layer convolution is shown, M represents the number of training samples, and eta represents the learning rate.
In this way, the parameters can be updated with the gradient of the loss function, which is the direction in which the point changes the fastest, with the advantages of easy computation, less time consumption, and faster convergence on large datasets.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. A SAR image classification method of pixel level statistical description learning is characterized by comprising the following steps:
s1, inputting the target SAR image into the SAR image classification model;
s2, extracting the pixel-level statistical description feature z of the target SAR image by a discrimination sub-network in the SAR image classification modelps；
S3, extracting structural mode description characteristic z of target SAR image by mode sub-network in SAR image classification modelpa；
S4 fusion module in SAR image classification modelCharacterizing pixel-level statistics by zpsAnd structural Pattern description feature zpaFusing to obtain an image description characteristic z of the target SAR image;
and S5, generating a classification result of the target SAR image based on the image description characteristics by a Softmax layer in the SAR image classification model.
2. The method for classifying SAR images for pixel-level statistical description learning according to claim 1, wherein the step S2 comprises:
s201, extracting a pixel value mean value mu of the target SAR image based on the following formulaxSum pixel value standard deviation σx：
In the formula, xiExpressing the pixel value of the ith pixel point of the target SAR image, and expressing the number of the pixel points of the target SAR image by n;
s202, based on the following formula, the method is used for muxAnd σxCarrying out scale and translation transformation to obtain corresponding characteristics zμAnd zσ：
zμ＝wμμx+bμ
zσ＝wσσx+bσ
In the formula, zμ∈V，zσE is V, and V is a high-dimensional mapping space; w is aμAnd bμRespectively represent muxCorresponding scale and translation transformation vectors, wμ＝[wμ1,wμ2,...,wμD]T，bμ＝[bμ1,bμ2,...,bμD]T，bμdAnd wμdRespectively represent muxD-th value of (D) corresponds to the scale and translation transformation parameter, D-1, 2, …, D denotes μxThe maximum dimension of; w is aσAnd bσRespectively represent sigmaxCorresponding scale and translation transformation parameters, wσ＝[wσ1,wσ2,...,wσD]T，bσ＝[bσ1,bσ2,...,bσD]T，bσdAnd wσdRespectively represent sigmaxThe scale and translation transformation parameters corresponding to the d-th value of (a);
s203, pair z based on the following formulaμAnd zσAdaptive optimization and nonlinear processing are carried out to generate pixel-level statistical description characteristics zps：
In the formula (I), the compound is shown in the specification,
ReLU (-) is a modified linear activation unit function,
and
respectively represents zpsThe corresponding weight matrix and the bias vector,
representing an M-dimensional linear space of,
representing an M x 2D dimensional linear space.
3. The method for classifying SAR images through pixel-level statistical description learning according to claim 1, wherein the pattern sub-network comprises 4 convolutional layers with all the activation functions being ReLU functions, and the processing formula of the kth convolutional layer is as follows:
zk＝H(Wkzk-1+bk)
in the formula, zkDenotes the output of the kth convolutional layer, k is 1,2,3,4, z0For a target SAR mapLike, H (-) denotes a composite function of the ReLU activation map and the pooling function, WkAnd bkRespectively represents zkThe corresponding weight matrix and the bias vector,
zpafeatures are described for the structural modes.
4. The method for classifying SAR image of pixel level statistical description learning according to claim 1, wherein in step S4, the pixel level statistical description feature z is based on the following formulapsAnd structural Pattern description feature zpaAnd (3) fusing to obtain an image description characteristic z of the target SAR image:
z＝ReLU(Wpszps+Wpazpa)
wherein ReLU (. cndot.) is a modified linear activation unit function, WpsAnd WpaAre each zpsAnd zpaAnd (4) corresponding weight matrix.
5. The method for classifying SAR images learned by pixel-level statistical description of claim 3, further comprising:
and S6, after the SAR image classification model training is finished each time, optimizing the parameters of the SAR image classification model by using a random gradient descent algorithm, wherein the optimization target is to find the minimum average loss function.
6. The method for classifying SAR image of pixel level statistical description learning according to claim 5, wherein in step S6, the parameter optimization of the pattern sub-network is performed based on the following formula:
in the formula, Xn'Denotes the nth' sample, yn'And
are each Xn'The true tag and the estimated tag of (a),
in order to be a function of the loss,
in the formula (I), the compound is shown in the specification,<·>and ln (·) respectively represent the inner product sum logarithm operation, Yn'Representing a genuine label yn'Label vector based on One-Hot coding, an'Represents Xn'The output vector after passing through the Softmax layer, the jth output of the Softmax layer is
Comprises the following steps:
in the formula, zjThe j input of the Softmax layer, wherein M represents the input number of the Softmax layer;
the update formula of the weight parameter of the k-th layer convolution in the pattern sub-network is as follows:
in the formula (I), the compound is shown in the specification,
a weight parameter representing the k-th layer convolution after the i +1 th layer is suboptimal,
a weight parameter representing the ith sub-optimized k-th layer convolution,
the gradient of the weight parameter of the loss function relative to the k-th layer convolution is shown, M represents the number of training samples, and eta represents the learning rate.
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