CN112949682A - SAR image classification method for feature level statistical description learning - Google Patents

SAR image classification method for feature level statistical description learning Download PDF

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CN112949682A
CN112949682A CN202110112799.3A CN202110112799A CN112949682A CN 112949682 A CN112949682 A CN 112949682A CN 202110112799 A CN202110112799 A CN 202110112799A CN 112949682 A CN112949682 A CN 112949682A
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刘新龙
邓磊
蒋仕新
李韧
王笛
张廷萍
杨建喜
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Chongqing Jiaotong University
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Abstract

The invention discloses a SAR image classification method for feature level statistical description learning, which comprises the following steps: inputting a target SAR image into an SAR image classification network; extracting characteristic elements with middle-layer semantics of the target SAR image by the convolutional layer; the feature statistical layer extracts a statistical element vector of the target SAR image based on a feature element with middle-layer semantics; the nonlinear and linear conversion layer generates a feature level statistical description vector of the target SAR image based on the statistical primitive vector; and the Softmax layer generates a classification result of the target SAR image based on the feature level statistical description vector. Compared with the traditional CNN method, the SAR image classification method based on the feature level statistics is not only dedicated to structural feature learning of the SAR image, but also particularly considers feature level statistics characteristics of the SAR image in the feature learning process, integrates feature learning and statistical analysis, and can effectively solve the problem of insufficient generalization capability when the CNN method is used for SAR image classification.

Description

SAR image classification method for feature level statistical description learning
Technical Field
The invention relates to the technical field of image processing, in particular to an SAR image classification method for feature level statistical description learning.
Background
Synthetic Aperture Radar (SAR) can acquire rich information of a ground object target all day long, but a coherent imaging mechanism of the SAR enables an SAR image to present an inherent random image mode, so that statistical analysis of the SAR image is important for extracting information such as geography, biology and the like. Under a coherent imaging mechanism, each pixel point of the SAR image is formed by coherent superposition of a plurality of scattering center echoes in a corresponding resolution unit. Due to the randomness of the distribution of the positions of the scattering centers in the resolution unit, the echoes of the scattering centers have random phases, so that the echoes of a plurality of scattering centers also have randomness after being coherently superposed and synthesized. The coherent imaging mechanism enables the SAR image to show a random fluctuation phenomenon at a pixel level, and the SAR echo signal has an extremely low signal-to-noise ratio. Furthermore, the backscattered echoes of the target under the coherent imaging mechanism are very sensitive to the target properties such as shape, orientation, dielectric properties and humidity, and the backscattering of the terrestrial target generally tends to be highly complex and highly random. In addition, since the ground object itself usually has diversity, the spatial distribution of its image elements is also random. Therefore, statistical analysis is crucial for information extraction of SAR images. At present, a processing method based on statistical analysis is widely applied to SAR image analysis. Statistical modeling is an effective method for statistical analysis of low-resolution SAR images, such as K-distribution and Gamma mixed distribution models. The above statistical model is a pixel-level based statistical analysis method, which is used for statistical modeling of the SAR echo signal amplitude (or intensity). For high resolution SAR images, many texture-based analysis methods have emerged as structures and geometric features in the images become more prominent. For example: and extracting a second-order statistic analysis method for SAR image description based on the gray level co-occurrence matrix. And similarly, performing statistical analysis on the low-layer or middle-layer characteristic elements based on a scale invariant feature extraction (SAR-SIFT) and wavelet element and sparse coding method, and taking a statistical histogram of the characteristic elements as the statistical description of the SAR image. The characteristic level statistical analysis method aims at performing statistical analysis on SAR images from different characteristic level layers.
In recent years, a convolutional neural network method based on autonomous learning high-level semantic feature description is gradually applied to an interpretation task of SAR images. In order to solve the problem of insufficient robustness of a CNN (Convolition Neural Network, CNN) model to speckle noise, researchers provide a CNN method based on speckle noise invariance constraint, and extract robust CNN feature description for SAR target identification. And (3) aiming at the over-fitting problem of the CNN model under the condition of limited training samples, researchers propose a full convolution network based on a Dropout layer to be used for the classification task of the SAR image. Similarly, researchers propose a deep memory CNN method based on information recording and parameter migration technology, which is used for relieving the over-fitting problem in the SAR target recognition task under a small sample. In addition, the related research starts to consider the unique special properties of the SAR data, and researches the CNN method which embodies the inherent properties of the SAR data. The analysis method based on the CNN autonomous feature learning is an important milestone for the development of the SAR image processing field and gradually becomes an important direction for research. However, the CNN feature extraction method does not consider the feature level statistical characteristics of the SAR image, and generally has a problem of insufficient generalization capability.
Therefore, there is a need for a new technical solution for solving the problem of insufficient generalization capability when classifying SAR images by using the CNN method.
Disclosure of Invention
Aiming at the defects of the prior art, the problems to be solved by the invention are as follows: the generalization capability of the CNN method for SAR image classification is insufficient.
In order to solve the technical problems, the invention adopts the following technical scheme:
a SAR image classification method for feature level statistical description learning comprises the following steps:
s1, inputting the target SAR image into an SAR image classification network;
s2, extracting characteristic elements with middle-layer semantics of the target SAR image by the convolution layer in the SAR image classification network;
s3, extracting a statistical element vector of the target SAR image based on a characteristic element with middle-layer semantics by a characteristic statistical layer in the SAR image classification network;
s4, generating a feature level statistical description vector of the target SAR image based on the statistical primitive vector by a nonlinear and linear conversion layer in the SAR image classification network;
and S5, generating a classification result of the target SAR image based on the characteristic-level statistical description vector by a Softmax layer in the SAR image classification network.
Preferably, in step S2, the feature primitives z with middle-level semantics of the target SAR image are extracted based on the following formula:
Figure BDA0002919720160000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002919720160000022
representing the complex function containing the ReLU and the max pooling, w and b representing the weight vector and the bias term of the convolutional layer, respectively, and x representing the input of the convolutional layer.
Preferably, in step S2, the statistical primitive vector S of the target SAR image is extracted based on the following formula:
Figure BDA0002919720160000023
in the formula (I), the compound is shown in the specification,
Figure BDA0002919720160000024
and
Figure BDA0002919720160000025
respectively representing first-order and second-order statistical element vectors of corresponding characteristic channels;
Figure BDA0002919720160000026
and
Figure BDA0002919720160000027
respectively represent z'jCorresponding mean and variance, j 1,2, … C, feature maps of feature primitives with mid-level semantics are denoted as
Figure BDA0002919720160000031
H. W and C respectively represent the height, width and number of channels of Z, Zj＝[z1,z2,...,zm]TFor the vectorized representation of the jth characteristic channel,
Figure BDA0002919720160000032
preferably, in step S3, the feature level statistical description vector h of the target SAR image is generated based on the following formula:
h＝ReLU(Ws+B)
in the formula (I), the compound is shown in the specification,
Figure BDA0002919720160000033
and
Figure BDA0002919720160000034
respectively representing a weight matrix and an offset vector for high-dimensional feature mapping, wherein P is more than 2C.
Preferably, the SAR image classification network is optimized based on the following formula:
Figure BDA0002919720160000035
in the formula, wkAnd bkWeight matrix and bias vector, y, representing the k-th convolutional layernDenotes the nth sample XnThe label of (a) is used,
Figure BDA0002919720160000036
denotes the nth sample XnN denotes the total number of samples,
Figure BDA0002919720160000037
represents a cross-entropy loss function of the entropy of the sample,
Figure BDA0002919720160000038
Ynindicating label ynVector based on One-Hot coding, anRepresenting the output vector of the Softmax layer.
The SAR image shows randomness of feature level, but the CNN feature extraction does not consider the feature level statistical characteristics of the SAR image. In order to overcome the defects of the prior art, the invention provides the SAR image classification method for feature level statistical description learning. Firstly, extracting feature primitives with middle-layer semantics based on the multilayer convolution layer to describe the structure information of the input image, wherein the middle-layer feature primitives describe image modes with different middle-layer semantics in the SAR image. Then, the feature statistical layer performs statistical analysis on the feature map of the middle-layer feature primitive, and extracts the statistical characteristics of the middle-layer features including first-order and high-order statistical primitive descriptions. And finally, learning the characteristic-level statistical description of the SAR image by the nonlinear and linear conversion layers based on the statistical primitives under the constraint of minimized classification errors, and obtaining a classification result through a Softmax layer. Compared with the traditional CNN method, the SAR image classification method based on the feature level statistics is not only dedicated to structural feature learning of the SAR image, but also particularly considers feature level statistics characteristics of the SAR image in the feature learning process, integrates feature learning and statistical analysis, and can effectively solve the problem of insufficient generalization capability when the CNN method is used for SAR image classification.
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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 illustration of a feature level statistical description learned SAR image classification method disclosed in the present invention.
FIG. 2 is a schematic diagram of training optimization of the SAR image classification network disclosed in the present invention.
Fig. 3 is a schematic diagram of a convolutional layer feature learning process of the SAR image classification network disclosed by 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 feature level statistical description learning, which comprises the following steps:
a SAR image classification method for feature level statistical description learning comprises the following steps:
s1, inputting the target SAR image into an SAR image classification network;
s2, extracting characteristic elements with middle-layer semantics of the target SAR image by the convolution layer in the SAR image classification network;
s3, extracting a statistical element vector of the target SAR image based on a characteristic element with middle-layer semantics by a characteristic statistical layer in the SAR image classification network;
s4, generating a feature level statistical description vector of the target SAR image based on the statistical primitive vector by a nonlinear and linear conversion layer in the SAR image classification network;
and S5, generating a classification result of the target SAR image based on the characteristic-level statistical description vector by a Softmax layer in the SAR image classification network.
Compared with the traditional CNN method, the SAR image classification method based on the feature level statistics is not only dedicated to structural feature learning of the SAR image, but also particularly considers feature level statistics characteristics of the SAR image in the feature learning process, integrates feature learning and statistical analysis, and can effectively solve the problem of insufficient generalization capability when the CNN method is used for SAR image classification.
In step S2, in a specific implementation, the feature primitive z with middle-level semantics of the target SAR image is extracted based on the following formula:
Figure BDA0002919720160000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002919720160000042
representing the complex function containing the ReLU and the max pooling, w and b representing the weight vector and the bias term of the convolutional layer, respectively, and x representing the input of the convolutional layer.
Under the non-strict definition, the learning process of the middle layer characteristic primitive based on the convolutional layer can be interpreted as the matching filtering in the broad sense. Specifically, the output z of the convolutional layer can be formulated as:
z＝wTx+b
wherein x represents the convolutional layer input, (. C)TRepresenting the transposed transform, w and b represent the weight vector and bias term of the convolutional layer, respectively. The offset term in the neglect equation is then wTx＝<w,x>Is an inner product operation in Euclidean space, which can be interpreted as a broad matched filter per se. Then, the physical meaning of formula-based convolutional layer feature extraction can be interpreted as: by changing the weights w to match the implicit pattern in the data x. FIG. 3 shows a schematic diagram of the feature learning process for convolutional layers, in which three weight vectors w for matching hidden patterns in the input x are given1、w2And w3. It can be seen that the weight vector w2Is superior to w1And w3Due to w2Angle theta with input x2Less than weight vector w1And w3Angle theta with x1And theta3. In the invention, the problem of nonlinear mapping and characteristic translation invariance is considered in the characteristic extraction based on the convolutional layer, so that modified linear unit activation mapping (ReLU) and maximum pooling processing are added in the convolutional layer.
With the increase of the number of the convolution layers, the SAR image classification network extracts SAR image feature primitives with gradually enhanced feature semantics. Similar to the standard CNN model, the convolutional layer of the SAR image classification network also employs a local connection and weight sharing mechanism. That is to say, the convolution layer output characteristic diagram of the SAR image classification network adopts a local connection mode with the previous layer output through the weight matrix, and each element in the output characteristic diagram shares the same weight. Therefore, each convolution output channel feature map of the SAR image classification network only describes one specific image pattern in the input image, i.e., the SAR image classification network extracts feature primitives for the input SAR image description. Moreover, as the number of convolution layers increases, the downsampling of the pooling process increases the receptive field of the current layer feature extraction, i.e. the feature semantics are gradually enhanced. A global description above low level semantics and not an image may be considered a middle level semantic feature. Therefore, the convolution layer is added, and under the condition of controlling non-global image description, the SAR image classification network can extract the SAR image feature primitive with middle-layer semantics.
In step S2, in a specific implementation, the statistical primitive vector S of the target SAR image is extracted based on the following formula:
Figure BDA0002919720160000051
in the formula (I), the compound is shown in the specification,
Figure BDA0002919720160000052
and
Figure BDA0002919720160000053
respectively representing first-order and second-order statistical element vectors of corresponding characteristic channels;
Figure BDA0002919720160000054
and
Figure BDA0002919720160000055
respectively represent z'jCorresponding mean and variance, j 1,2, … C, feature maps of feature primitives with mid-level semantics are denoted as
Figure BDA0002919720160000056
H. W and C respectively represent the height, width and number of channels of Z, Zj＝[z1,z2,...,zm]TFor the vectorized representation of the jth characteristic channel,
Figure BDA0002919720160000057
in step S3, in a specific implementation, the feature level statistical description vector h of the target SAR image is generated based on the following formula:
h＝ReLU(Ws+B)
in the formula (I), the compound is shown in the specification,
Figure BDA0002919720160000058
and
Figure BDA0002919720160000059
respectively representing a weight matrix and an offset vector for high-dimensional feature mapping, wherein P is more than 2C.
After the statistical primitive vector s of the middle-layer features is obtained, learning ethics are learned based on statistical description, and feature-level statistical description of the SAR image is learned based on s under the constraint of minimized classification errors. Specifically, the method autonomously maps s to a high-dimensional space through nonlinear and linear transformation, and autonomously constructs a function of a feature-level statistical primitive under the constraint of minimized classification errors, so as to learn the feature-level statistical description of the SAR image.
In the invention, in order to ensure the high-dimensional mapping of the feature level statistical description vector h, the dimension of the high-dimensional feature space needs to satisfy P & gt 2C. And changing the weight matrix and the bias vector of the high-dimensional mapping to obtain the feature level statistical description vector h under different mapping relations. Since h is a function of the statistical primitive, the vector h actually constitutes a feature-level statistical distribution parameter space. Then, the description of the SAR image based on the vector h embodies the statistical properties of the SAR image in the feature space.
As shown in fig. 2, in specific implementation, the SAR image classification network is optimized based on the following formula:
Figure BDA0002919720160000061
in the formula, wkAnd bkWeight matrix and bias vector, y, representing the k-th convolutional layernDenotes the nth sample XnThe label of (a) is used,
Figure BDA0002919720160000062
denotes the nth sample XnN denotes the total number of samples,
Figure BDA0002919720160000063
represents a cross-entropy loss function of the entropy of the sample,
Figure BDA0002919720160000064
Ynindicating label ynVector based on One-Hot coding, anRepresenting the output vector of the Softmax layer.
The optimization problem described above can be solved by a stochastic gradient descent algorithm. In the stochastic gradient descent algorithm, solving the gradient of the loss function with respect to the optimization parameters is a key step of the optimization algorithm. The invention constructs a feature statistics layer from which the gradient of the loss function with respect to the input Z of the layer needs to be deduced in order to back-propagate the classification error. The error propagation for this feature statistics layer is derived below. Note the book
Figure BDA0002919720160000065
As a function of loss
Figure BDA0002919720160000066
Gradient with respect to Z, i.e.
Figure BDA0002919720160000067
Partial differential of
Figure BDA0002919720160000068
The derivation of (c) takes into account two aspects:
(1) when Ws + B is less than or equal to 0, i.e. the characteristic vector h shown in the formula is 0, partial differential
Figure BDA0002919720160000069
(2) When Ws + B>Partial differential at 0
Figure BDA00029197201600000610
Comprises the following steps:
Figure BDA00029197201600000611
in the formula (I), the compound is shown in the specification,
Figure BDA00029197201600000612
representing loss function
Figure BDA00029197201600000613
Relative to the input partial differential of the Softmax layer, the partial differential
Figure BDA00029197201600000614
Can be found by a back propagation algorithm. For the
Figure BDA0002919720160000071
The following two cases are considered for derivation of (1):
(1) when q ≠ j (q ≠ 1, 2.., C),
Figure BDA0002919720160000072
(2) when q is equal to j,
Figure BDA0002919720160000073
comprises the following steps:
Figure BDA0002919720160000074
from the derivation of the sum of the equations, the gradient can be determined
Figure BDA0002919720160000075
Then, the optimization of the SAR image classification network can be solved by a stochastic gradient descent algorithm.
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 (5)

1. A SAR image classification method for feature level statistical description learning is characterized by comprising the following steps:
s1, inputting the target SAR image into an SAR image classification network;
s2, extracting characteristic elements with middle-layer semantics of the target SAR image by the convolution layer in the SAR image classification network;
s3, extracting a statistical element vector of the target SAR image based on a characteristic element with middle-layer semantics by a characteristic statistical layer in the SAR image classification network;
s4, generating a feature level statistical description vector of the target SAR image based on the statistical primitive vector by a nonlinear and linear conversion layer in the SAR image classification network;
and S5, generating a classification result of the target SAR image based on the characteristic-level statistical description vector by a Softmax layer in the SAR image classification network.
2. The method for classifying SAR images for feature-level statistical description learning as claimed in claim 1, wherein in step S2, the feature primitives z with middle-level semantics of the target SAR image are extracted based on the following formula:
Figure FDA0002919720150000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002919720150000012
representing the complex function containing the ReLU and the max pooling, w and b representing the weight vector and the bias term of the convolutional layer, respectively, and x representing the input of the convolutional layer.
3. The method for classifying SAR images for feature-level statistical description learning according to claim 1, wherein in step S2, the statistical primitive vector S of the target SAR image is extracted based on the following formula:
s＝[μ，σ2]T
in the formula (I), the compound is shown in the specification,
Figure FDA0002919720150000018
and
Figure FDA0002919720150000013
respectively representing first-order and second-order statistical element vectors of corresponding characteristic channels;
Figure FDA0002919720150000019
and
Figure FDA0002919720150000015
respectively represent z'jCorresponding mean and variance, j 1,2, … C, feature maps of feature primitives with mid-level semantics are denoted as
Figure FDA0002919720150000016
H. W and C respectively represent the height, width and number of channels of Z, Zj＝[z1，z2，...，zm]TFor the vectorized representation of the jth characteristic channel,
Figure FDA0002919720150000017
4. the method for classifying SAR images for feature-level statistical description learning according to claim 1, wherein in step S3, the feature-level statistical description vector h of the target SAR image is generated based on the following formula:
h＝ReLU(Ws+B)
in the formula (I), the compound is shown in the specification,
Figure FDA0002919720150000021
and
Figure FDA0002919720150000022
respectively representing a weight matrix and an offset vector for high-dimensional feature mapping, wherein P is more than 2C.
5. The method of feature level statistical description learned SAR image classification of claim 1, wherein the SAR image classification network is optimized based on the following formula:
Figure FDA0002919720150000023
in the formula, wkAnd bkWeight matrix and bias vector, y, representing the k-th convolutional layernDenotes the nth sample XnThe label of (a) is used,
Figure FDA0002919720150000024
denotes the nth sample XnN denotes the total number of samples,
Figure FDA0002919720150000025
represents a cross-entropy loss function of the entropy of the sample,
Figure FDA0002919720150000026
Ynindicating label ynVector based on One-Hot coding, anRepresenting the output vector of the Softmax layer.
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