CN109190684B - SAR image sample generation method based on sketch and structure generation countermeasure network - Google Patents

SAR image sample generation method based on sketch and structure generation countermeasure network Download PDF

Info

Publication number
CN109190684B
CN109190684B CN201810928531.5A CN201810928531A CN109190684B CN 109190684 B CN109190684 B CN 109190684B CN 201810928531 A CN201810928531 A CN 201810928531A CN 109190684 B CN109190684 B CN 109190684B
Authority
CN
China
Prior art keywords
sketch
sar image
block
network
representing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810928531.5A
Other languages
Chinese (zh)
Other versions
CN109190684A (en
Inventor
刘芳
李玲玲
王哲
焦李成
陈璞花
郭雨薇
马文萍
杨淑媛
侯彪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xidian University
Original Assignee
Xidian University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xidian University filed Critical Xidian University
Priority to CN201810928531.5A priority Critical patent/CN109190684B/en
Publication of CN109190684A publication Critical patent/CN109190684A/en
Application granted granted Critical
Publication of CN109190684B publication Critical patent/CN109190684B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

The invention provides an SAR image sample generation method based on a sketch and structure generation countermeasure network, which mainly solves the problem of sample imbalance in SAR image semantic segmentation, and comprises the following implementation steps: (1) the SAR image is subjected to sketch to obtain a sketch map; (2) extracting a small extremely inhomogeneous region according to a regional map of the SAR image; (3) constructing a paired data set in the shape of a sketch block-SAR image block; (4) selecting samples in the data set to form a training set and a testing set; (5) constructing a generation countermeasure network based on sketch information and structural constraints; (6) alternately training the sketch fitting network, the judging network and the generating network in sequence through sketch line loss, confrontation loss and generator loss; (7) inputting a test sketch block to a trained generation network to obtain a generated SAR image block; the SAR image sample inosculated with the ground object structure of the original SAR image can be generated according to the sketch map, and the problem of sample imbalance of classification of extremely uneven regions of the SAR image can be solved.

Description

SAR image sample generation method based on sketch and structure generation countermeasure network
Technical Field
The invention belongs to the technical field of image generation, and further relates to an image generation method for deep learning and generating confrontation network GANs (genetic adaptive networks) and synthetic Aperture radar (synthetic Aperture radar) images.
Background
Sample imbalance is a common and difficult to avoid problem in synthetic aperture radar SAR image classification and segmentation. Common machine learning models usually assume that samples are unbiased samples of true distributions, while most models have very poor recall of rare classes on the test set due to the extreme difference in samples between rare and rich classes in image classification. Similarly, the existence of small regions puts higher requirements on the segmentation technology of the SAR image, if the image segmentation is regarded as a classification problem at the image pixel level, the number of samples of the small regions is far less than that of the regions with larger areas, so that the small regions of the test set tend to be divided into large regions, especially for extremely heterogeneous regions in the SAR image, such as forests, urban areas and the like, the number of samples of the extremely heterogeneous regions is extremely different, and the difficulty of the SAR image segmentation is greatly increased.
In the face of the sample imbalance problem of SAR image classification and segmentation, the existing solutions are generally divided into the following two categories: under-sampling and over-sampling at the data level, and class weight changes at the model level.
Under-sampling and over-sampling at the data level mean that the class distribution of samples is changed through a certain strategy so as to achieve the purpose of converting the samples with unbalanced distribution into the samples with relatively balanced distribution.
And the sample undersampling is to randomly select a small number of samples from a plurality of classes and combine the samples with the original small number of classes to form a new training set. Sample oversampling, in contrast, performs sample enhancement by copying samples or performing various affine transformations on a few classes of samples. The method has the disadvantages that the undersampling of the samples can reduce the scale of the training set, thereby causing information loss, and the samples which are not sampled have important information; the samples generated by common affine transformation adopted in the sample oversampling are almost the same as original images, or are seriously distorted, even do not accord with an SAR image imaging mechanism, and the overfitting of the model is easily caused.
The class weight change at the model level refers to that the cost function of the learning model is modified so that the learning model is more sensitive to the cost of the rare class. The method has the disadvantages that the complexity of the model is increased, the parameter adjustment of the model is more difficult, and the classification problem of extremely different sample numbers is invalid.
Disclosure of Invention
The invention aims to provide an SAR image sample generation method for generating a countermeasure network based on sketch and structure, which solves the defect that classification and segmentation cannot be carried out on an SAR image due to the existing sample imbalance.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides an SAR image sample generation method based on a sketch and structure generation countermeasure network, which comprises the following steps:
step 1, performing sketch processing on an original SAR image I to obtain a sketch I of the SAR image Is；
Step 2, sketch I in the step 1sCarrying out regionalization to obtain a regional image, and extracting an extremely heterogeneous region set of an original SAR image I from the regional image;
step 3, constructing a paired data set in the form of a sketch block-SAR image block according to the extremely heterogeneous region set of the original SAR image I obtained in the step 2;
step 4, in the data set constructed in the step 3, randomly selecting 70% of data samples as a training data set, and using the rest data samples as a testing data set;
step 5, constructing a generation countermeasure network of sketch information and structural constraint, wherein the generation countermeasure network comprises a generator network, a discriminator network and a sketch fitter network;
step 6, training the generated countermeasure network constructed in the step 5 by using the training data set in the step 4;
and 7, inputting the test data set in the step 4 into the generated countermeasure network obtained by training in the step 6 to obtain a generated SAR image block.
Preferably, in step 1, the specific method for performing the sketch processing on the original SAR image I is as follows:
obtaining a sketch model of the original SAR image according to the distribution characteristics of the original SAR image I, and performing sketch processing on the original SAR image I by using the sketch model to obtain a sketch I of the original SAR image Is。
Preferably, in step 2, the specific method for extracting the extremely inhomogeneous region set of the original SAR image I is as follows:
firstly, the sketch I obtained in the step 1 is regionalized by sketch linesCarrying out regionalization treatment to obtain a regional image of the original SAR image, wherein the regional image comprises an aggregation region, a sketch-free region and a structural region;
secondly, mapping the regional image into an original SAR image I to obtain a mixed pixel subspace, a homogeneous pixel subspace and a structural pixel subspace of the SAR image I;
and finally, extracting a mixed pixel subspace of the SAR image I as an extremely heterogeneous region set of the original SAR image I, wherein the mixed pixel subspace is generally composed of a plurality of extremely heterogeneous regions which are not communicated with each other and have different sizes.
Preferably, in step 3, the specific steps of constructing the paired data sets in the form of "sketch block-SAR image block" are as follows:
1, selecting a very inhomogeneous region from the very inhomogeneous regions obtained in the step 2, wherein the very inhomogeneous region refers to any one of 1/3 extremely inhomogeneous regions with the area smaller than the area of the maximum very inhomogeneous region in the very inhomogeneous region set; then, carrying out interval dicing on the extremely inhomogeneous region by using a rectangular window with the size of 128 multiplied by 128, wherein the cutting interval size is 32, and then obtaining a plurality of cut SAR image blocks y;
step 2, according to the position of the SAR image block y in the original SAR image I, obtaining a sketch I from the step 1sAnd drawing a sketch block x corresponding to the position of the SAR image block y, and constructing a paired data set in the form of a sketch block-SAR image block.
Preferably, in step 5, the generator network includes an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a first maximum pooling layer, a fifth convolution layer, a sixth convolution layer, a second maximum pooling layer, a first deconvolution layer, a second deconvolution layer, a third deconvolution layer, a seventh convolution layer, and an output layer, which are connected in sequence; wherein, the input of the generator network is 8-dimensional Gaussian random noise z and a sketch block x in a training set, and the output is the SAR image block
Figure BDA0001765960240000031
The discriminator network comprises an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, a sixth convolution layer, a seventh convolution layer and a classifier which are connected in sequence; wherein, the input of the discriminator network is SAR image block y in the training set and the generated SAR image block
Figure BDA0001765960240000041
The output is a single node which represents the probability that the SAR image block conforms to the distribution of the real SAR sample;
the sketch fitter network comprises an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a first deconvolution layer, a second deconvolution layer, a fourth convolution layer and an output layer which are connected in sequence; wherein, the input of the sketch fitting device network is SAR image block y in the training set and SAR image block generation
Figure BDA0001765960240000042
The output is a fitted sketch block of the SAR image.
Preferably, in step 6, the specific method for training the constructed generation countermeasure network is as follows:
first, a sketch line loss function L is constructedSAntagonistic loss function LGANAnd a generator loss function L;
secondly, by the line loss function LSAntagonistic loss function LGANAnd a generator loss function L and the training data set obtained in the step 4 and the 8-dimensional Gaussian noise vector generated randomly are combined with a small batch random gradient descent method to alternately train the sketch fitter network, the discriminator network and the generator network in sequence, and finally the weight values of the trained sketch fitter network, the discriminator network and the generator network are obtained.
Preferably, the generator loss function L is a geometric window loss function LWSketch line loss function LSAnd a loss-fighting function LGAGThe weighted sum yields:
Figure BDA0001765960240000043
wherein, thetaGRepresenting the parameters of the generator G, thetaDRepresenting parameters of a discriminator D, wherein alpha, beta and gamma are all parameters; specifically, the method comprises the following steps:
geometric window loss function LWExpressed as:
Figure BDA0001765960240000044
wherein, thetaGParameters representing generator G, pz、pXAnd pYRespectively representing the distribution of noise z, sketch blocks x in the training set and SAR image blocks y in the training set, G (z | x) representing a SAR image generated conditioned on the original SAR image sketch blocks, | indicating an element-by-element multiplication, | | > |1L representing a vector1Norm, E [. C]Expressing an average value; n is a radical ofxThe number of line segments.
Preferably, geometric window lossesFunction LWThe design method specifically comprises the following steps:
step 1, constructing a geometric structure window by utilizing any sketch line segment in a sketch block x in a training set, wherein the geometric structure window surrounds the sketch line segment, an area surrounded by the geometric structure window is defined as a structure area, and an area outside the window is defined as a non-structure area;
step 2, obtaining N of SAR image block y in the training set from the geometric structure window in step 1xBinary split mask matrix
Figure BDA0001765960240000051
Wherein the content of the first and second substances,
Figure BDA0001765960240000052
the matrix size of the SAR image block y is the same as that of the SAR image block y, the value of the structural area of the SAR image block is 1, and the value of the non-structural area of the SAR image block is 0;
step 3, binary division mask matrix M obtained from step 2xTo obtain a geometric structure window loss function LWThe mathematical expression of (a):
Figure BDA0001765960240000053
wherein, thetaGParameters representing generator G, pz、pXAnd pYRespectively representing the distribution of 8-dimensional Gaussian noise z, the sketch block x in the training set and the SAR image block y in the training set, G (z | x) representing the SAR image block generated with the condition of the sketch block x in the training set, indicating element-by-element multiplication, < | >, respectively1L representing a vector1Norm, E [. C]Indicating averaging.
Preferably, the sketch line loss function L used in training the generatorSThe design method specifically comprises the following steps:
sketch line loss function LSIs expressed as a sketch block x of the original SAR image and a fitting sketch block for generating the SAR image
Figure BDA0001765960240000054
After weightingL of2Loss:
Figure BDA0001765960240000055
wherein, thetaGParameters representing generator G, pzAnd pXRespectively representing the distribution, E [ ·, of 8-dimensional Gaussian noise z and sketch blocks x in the training set]Indicating mean, y+And y-Respectively representing the inner part of the geometric structure window and the outer part of the geometric structure window, x in the SAR image block yiAnd
Figure BDA0001765960240000056
respectively representing original SAR image sketch block x and generating SAR image fitting sketch block
Figure BDA0001765960240000057
A value at a specified location i;
setting a sketch block x in a training set, and expressing the ratio lambda of the number of pixels of a sketch part of an SAR image block y in the training set to the total number of pixels of the image as follows:
Figure BDA0001765960240000061
setting a sample x of a sketch block of the original SAR image, 8-dimensional Gaussian random noise z, a generator G and a sketch fitting device S, and generating a fitting sketch block of the SAR image
Figure BDA0001765960240000062
Can be expressed as:
Figure BDA0001765960240000063
sketch loss function L 'used in training sketch fitter'SThe design method is as follows:
Figure BDA0001765960240000064
wherein, thetaSParameters, p, representing a sketch fitter SXAnd pYRespectively representing the distribution of the sketch blocks x and the SAR image blocks y in the training set, E [ ·]Indicating mean, y+And y-Respectively representing the inside part and the outside part of a geometric structure window in the SAR image block y, xiAnd
Figure BDA0001765960240000065
respectively representing original SAR image sketch block x and generating SAR image fitting sketch block
Figure BDA0001765960240000066
The definition of the value, λ, at the given position i is the sketch line loss function L used in training the generatorSThe design method of (1).
Preferably, the penalty function L is resistedGANThe design method specifically comprises the following steps:
the input of the discriminator D is SAR image block y in the training set and the generated SAR image block
Figure BDA0001765960240000067
Outputting the probability that the sample accords with the original SAR image distribution; wherein, according to the definition of the condition GAN, the loss resisting function LGANThe mathematical expression of (a) is:
Figure BDA0001765960240000068
wherein, thetaGRepresenting the parameters of the generator G, thetaDParameter, p, representing discriminator Dz、pXAnd pYRespectively representing the distribution of 8-dimensional Gaussian noise z, a sketch block x in a training set and an SAR image block y in the training set; g (z | x) represents a SAR image block generated conditioned on a sketch block x in the training set
Figure BDA0001765960240000069
D (y | x) represents the output of the discriminator when inputting SAR image block y in the training set, and D (G (z | x)) represents the input of the discriminator to generate SAR image block
Figure BDA00017659602400000610
An output of time; e [. C]Indicating averaging.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a SAR image sample generation method based on sketch and structure generation confrontation network, which obtains a generation confrontation network of sketch information and structure constraint by the steps 5 and 6, the network samples from a small extremely heterogeneous region in the SAR image mixed pixel subspace for unsupervised training, and the trained generator network is used for generating a sample which is matched with the original image structure and is distributed uniformly, thereby overcoming the defects that the sample generated by the existing oversampling technology is the same as the original image or seriously distorted and can not improve the training effect of a classification model, and leading the invention to better improve the problem of sample imbalance in SAR image segmentation;
meanwhile, step 5 also obtains a sketch fitter network, which is used for sampling and training from the original SAR image and the sketch obtained by the sketch model thereof, and the fitted sketch can be directly obtained from the SAR image by using the trained sketch fitter network, so that the defect that the sketch model adopted in the prior art is not tiny is overcome, the SAR image sketch line constraint can be applied to a system optimized by using a gradient descent method by adopting the method, and the sensitivity of the system to SAR image structure information is further improved;
furthermore, the training process of generating the confrontation network is guided by simultaneously using the geometric structure window loss function, the sketch line loss function and the confrontation loss function, so that the defect that the confrontation network is generated only by using the confrontation loss function constraint in the prior art without paying attention to the structural characteristics of the SAR image is overcome, the structural characteristics of the SAR image generated by the confrontation network can be effectively constrained and generated by adopting the method, and the fidelity of the generated sample is improved.
Drawings
FIG. 1 is a flow chart of generation of a countermeasure network for sketch information and structural constraints in the present invention, wherein a solid arrow represents forward propagation and a dashed arrow represents a backward propagation update generator G;
FIG. 2 is a schematic diagram of a network architecture of the present invention, wherein 2a is a network architecture of a generative model; 2b is the network architecture of the discriminant model; 2c is the network architecture of the sketch fitting model;
FIG. 3 is a schematic view of a geometric window of the present invention;
fig. 4 is a partial simulation result diagram of the present invention, wherein, viewed in columns, the first column from left to right is an original SAR image block, the second column is a sketch block of the original SAR image block, the third column is a generated SAR image block, and the fourth column is a fitting sketch block of the generated SAR image block.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the method for generating an SAR image sample based on a sketch and structure generation countermeasure network provided by the present invention includes the following steps:
step 1, performing sketch processing on an input original SAR image I to obtain a sketch I of the input SAR imagesSpecifically:
obtaining a sketch model of the input original SAR image according to the distribution characteristics of the input original SAR image I, and performing sketch processing on the input original SAR image by using the sketch model to obtain a sketch I of the input SAR images(ii) a Sketch IsThe method is a binary image, wherein the value of the sketch-able part is 1, which indicates that the brightness of the original SAR image I is suddenly changed at the position, and the value of the non-sketch part is 0, which indicates that the brightness of the original SAR image I is unchanged or slowly changed at the position.
The synthetic aperture radar SAR image sketch model used by the invention is a model proposed by Jie-Wu et al in 2014 in an article Local maximum geographic region search for SAR specific reduction with sketch-based geographic region search function in IEEE Transactions on Geoscience and Remote Sensing journal.
Step 2, sketch I in the step 1sRegionalizing to obtain a regional image, and extracting an extremely heterogeneous region set of the original SAR image I from the regional image, wherein the specific method comprises the following steps:
firstly, a sketch I is formed by adopting a sketch line regionalization methodsCarrying out regionalization treatment to obtain a regional image of the SAR image comprising an aggregation region, a sketch-free region and a structural region;
secondly, mapping the obtained region map to an original SAR image I to obtain a mixed aggregation structure ground object pixel subspace (hereinafter referred to as mixed pixel subspace), a homogeneous region pixel subspace (hereinafter referred to as homogeneous pixel subspace) and a structural pixel subspace of the original SAR image I;
and finally, extracting a mixed pixel subspace of the original SAR image I, wherein the subspace generally consists of a plurality of extremely heterogeneous regions which are different in size and are not communicated with one another.
The SAR image regionalization processing method used by the invention is a method proposed by an article 'historical semantic model and statistical mechanism based PolSAR image classification' published in Pattern Recognition journal in 2016 by Liu F and Shi J F and the like.
Step 3, constructing a paired data set in the form of a sketch block-SAR image block;
the specific steps of constructing the paired data sets in the form of "sketch blocks-SAR image blocks" are as follows:
step 1, selecting an extremely inhomogeneous region from the mixed pixel subspace obtained in the step 2, wherein the extremely inhomogeneous region refers to any extremely inhomogeneous region 1/3 with the area smaller than the area of the maximum extremely inhomogeneous region in the mixed pixel subspace; then, carrying out interval dicing on the extremely heterogeneous region by using a rectangular window with the size of 128 multiplied by 128, wherein the size of a cutting interval is 32, and then obtaining a cut SAR image block y;
step 2, according to the position of the SAR image block y in the original SAR image I, obtaining a sketch I from the step 1sThe method comprises the steps of extracting a sketch block x corresponding to the position y of an SAR image block, and constructing the sketch block-SAR image blockFor the data set.
And 4, selecting a sample.
Randomly selected 70% of the samples in the data set constitute the training data set, and the remaining 30% of the samples constitute the testing data set.
And 5, constructing a generation countermeasure network of sketch information and structural constraint.
Constructing a deep convolution neural network, inputting 8-dimensional Gaussian random noise z and a sketch block x in a training set, and outputting the deep convolution neural network as a generated SAR image block
Figure BDA0001765960240000091
And randomly initializing parameters of each convolution kernel in the network to obtain an initialized generator network.
FIG. 2 is a schematic diagram of a network architecture for a generative model, a discriminative model and a sketch-fitting model.
As shown in fig. 2a, the generator network includes an input layer, a first convolutional layer, a second convolutional layer, a third convolutional layer, a first max pooling layer, a fifth convolutional layer, a sixth convolutional layer, a second max pooling layer, a first deconvolution layer, a second deconvolution layer, a third deconvolution layer, a seventh convolutional layer, and an output layer, which are connected in sequence. The input of the input layer is 8-dimensional Gaussian noise and a sketch block with the size of 128 multiplied by 1, the filter sizes of 11 layers between the input layer and the output layer are respectively 3,3,3,3,1,3,3,3,3,3,3, and 32,32,64,64,80,192, 512,256,128,1, and the output layer outputs a gray scale map with the size of 128 multiplied by 1.
Constructing a deep convolutional neural network, and respectively converting SAR image blocks y and SAR image blocks in a training set
Figure BDA0001765960240000101
And as an input, outputting a single node which represents the probability that an input image block accords with the distribution of a real SAR sample, and randomly initializing the parameters of each convolution kernel in the network to obtain an initialized discriminator network.
As shown in fig. 2b, the discriminator network includes an input layer, a first convolutional layer, a second convolutional layer, a third convolutional layer, a fourth convolutional layer, a fifth convolutional layer, a sixth convolutional layer, a seventh convolutional layer and a classifier, which are connected in sequence. The input of the input layer is SAR image blocks of size 128 x 1, the filter sizes of 7 layers between the input layer and the output layer are respectively 4,4,4,4,4,4,1, the number of feature maps is respectively 32,64,128,256,512,1024,1, and the binary classifier outputs a scalar.
Constructing a deep convolutional neural network, and respectively converting SAR image blocks y and SAR image blocks in a training set
Figure BDA0001765960240000102
The input is the probability that each pixel in the SAR image block can be sketched (hereinafter referred to as the fitting sketch block of the SAR image), and the parameters of each convolution kernel in the network are initialized randomly to obtain the initialized sketch fitter network.
As shown in fig. 2c, the sketch fitter network comprises an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a first deconvolution layer, a second deconvolution layer, a fourth convolution layer and an output layer, which are connected in sequence. The input of the input layer is SAR image blocks with the size of 128 × 128 × 1, the filter sizes of 6 layers between the input layer and the output layer are respectively 7,3,3,3,3 and 7, the number of feature maps is respectively 32,64,128,64,32 and 1, and the output layer outputs gray-scale maps with the size of 128 × 128 × 1.
And 6, training and learning the generator network, the discriminator network and the sketch fitter network.
FIG. 1 is a flow diagram of generation of a countermeasure network for sketch information and structural constraints.
As shown in fig. 1, the line loss function L is plotted against the lineSAntagonistic loss function LGANAnd a generator loss function L, alternately training the sketch fitter network, the discriminator network and the generator network in sequence by using the training data set in the paired data sets of the sketch block-SAR image block constructed in the step 4 and the randomly generated 8-dimensional Gaussian noise vector by using a small batch random gradient descent method to obtain the weights of the trained sketch fitter network, the discriminator network and the generator network.
Said generator lossesLoss function L is a function of loss from a geometric windowWSketch line loss function LSAnd a penalty function LGANThe weighted sum yields:
Figure BDA0001765960240000111
wherein, thetaGRepresenting the parameters of the generator G, thetaDParameters representing the discriminator D, α, β and γ are parameters for balancing the 3 loss functions.
Fig. 3 is a schematic view of a geometry window.
As shown in FIG. 3, the geometry window loss function L used in training the generatorwThe design method is as follows:
step 1, constructing a geometric structure window by using the midpoint position, length and direction of any sketch line segment in a sketch block x in a training set, wherein the geometric structure window surrounds the sketch line segment and is in a rotating rectangle shape, the direction of the sketch line segment is taken as the rotating direction of the geometric structure window, the midpoint of the sketch line segment is taken as the gravity center of the geometric structure window, the width of the geometric structure window is 5, and the height is the sum of the length of the sketch line segment and 4;
step 2, defining a region surrounded by the geometric structure window constructed in the step 1 as a local geometric structure region with abrupt brightness change (hereinafter referred to as a structure region), and defining a region outside the window as a region with unchanged brightness or slowly changed brightness (hereinafter referred to as a non-structure region);
step 3, setting a sketch block x, 8-dimensional Gaussian noise z, an SAR image block y and a generator G as known, and assuming that the number of sketch line segments in the sketch block x is NxAccording to NxLine segment structure NxA geometric window. And taking a single geometric structure window, setting the value of the area in the window to be 1, and setting the values of other areas outside the window to be 0, and defining the binary matrix to be a binary segmentation mask matrix of the original SAR image block y. Thereby according to the above-mentioned NxN of original SAR image block y can be obtained through each geometric structure windowxBinary split mask matrix
Figure BDA0001765960240000112
Wherein the content of the first and second substances,
Figure BDA0001765960240000113
is the same as the SAR image block y, and has a value of 1 for the area inside the window and 0 for the other areas outside the window.
By dividing the mask matrix M in binaryxGeometric structure window loss function LWCan be expressed as an original SAR image block y and a generated SAR image block
Figure BDA0001765960240000114
In NxL of pixels in a structural area surrounded by a geometric structural window1The sum of the losses.
Geometric window loss function LWCan be expressed as:
Figure BDA0001765960240000121
wherein, thetaGParameters representing generator G, pz、pXAnd pYRespectively representing the distribution of 8-dimensional Gaussian noise z, the original SAR image sketch block samples x and the original SAR image block samples y, G (z |) representing the SAR image block generated conditioned on the original SAR image sketch block, indicating an element-by-element multiplication, | > | | > |1L representing a vector1Norm, E [. C]Indicating averaging.
Sketch line loss function L used in training generatorsSThe design method is as follows:
step 1, giving an original SAR image pixel-drawing block sample x, and expressing the ratio lambda of the number of pixels of a pixel-drawing part of the SAR image block sample y to the total number of pixels of the image as follows:
Figure BDA0001765960240000122
step 2, giving original SAR image pixel description block samplesx, 8-dimensional Gaussian random noise z, a generator G, a sketch fitter S, and a fitting sketch block for generating SAR images
Figure BDA0001765960240000123
Can be expressed as:
Figure BDA0001765960240000124
step 3, final sketch line loss function LsIs expressed as a sketch block x of the original SAR image and a fitting sketch block for generating the SAR image
Figure BDA0001765960240000125
Weighted L2Loss:
Figure BDA0001765960240000126
wherein, thetaGParameters representing generator G, pzAnd pXRespectively representing the distribution, E [ ·, of 8-dimensional Gaussian noise z and sketch blocks x in the training set]Indicating mean, y+And y-Respectively representing the inner part of the geometric structure window and the outer part of the geometric structure window, x in the SAR image block yiAnd
Figure BDA0001765960240000127
respectively representing original SAR image sketch block x and generating SAR image fitting sketch block
Figure BDA0001765960240000128
A value at a specified location i;
penalty function L used in training generatorsGANThe design method is as follows:
the input of the discriminator D is an original SAR image block sample y and a generated SAR image block sample
Figure BDA0001765960240000129
And outputting the probability that the sample accords with the original SAR image distribution. According toDefinition of the conditional GAN, will oppose the loss function LGANThe form of the zero sum game is expressed as follows:
Figure BDA0001765960240000131
wherein, thetaGRepresenting the parameters of the generator G, thetaDParameter, p, representing discriminator Dz、pXAnd pYRespectively representing the distribution of 8-dimensional Gaussian noise z, original SAR image sketch block samples x and original SAR image block samples y; g (z | x) represents an SAR image block generated by taking the original SAR image pixel description block x as a condition, D (y | x) represents the output of the discriminator when the original SAR image block sample is input by taking the original SAR image pixel description block x as a condition, and D (G (z | x)) represents the output of the discriminator when the SAR image block is generated by input; e [. C]Indicating averaging.
The sketch loss function used in training the sketch fitter is different from the sketch loss function used in training the generator. The sketch line loss function knows the original SAR image sketch block sample x, the original SAR image block sample y and the sketch fitter S, at this time, the fitting sketch block of the original SAR image
Figure BDA0001765960240000132
It should be expressed as:
Figure BDA0001765960240000133
the sketch line loss function L'SShould be expressed as the original SAR image sketch block x and the original SAR image fitting sketch block
Figure BDA0001765960240000134
Weighted L2Loss:
Figure BDA0001765960240000135
wherein, thetaSParameters representing a sketch fitter S，pXAnd pYRespectively representing the distribution of the sketch blocks x and the SAR image blocks y in the training set, E [ ·]Indicating mean, y+And y-Respectively representing the inside part and the outside part of a geometric structure window in the SAR image block y, xiAnd
Figure BDA0001765960240000136
respectively representing a sketch block x in a training set and generating a SAR image fitting sketch block
Figure BDA0001765960240000137
The definition of the value, λ, at the given position i is the sketch line loss function L used in training the generatorSThe design method of (1).
The method for alternately training the sketch fitter network, the discriminator network and the generator network in sequence by means of the small-batch random gradient descent method comprises the following specific steps:
step 1, setting a training batch size n to 64 and an iteration number k to 5000, and setting weight parameters alpha to 0.01, beta to 0.74 and gamma to 0.25 of three loss functions contained in a generator loss function;
step 2, from the training data set constructed in step 4: randomly sampling a batch of n samples { (X) in a { SAR image sketch block sample set X, SAR image block sample set Y }(1),y(1)),…,(x(n),y(n))}；
Step 3, from the noise distribution pzIn a batch of n samples { z }(1),…,z(n)}；
And 4, updating the sketch fitter network S by a small batch random gradient descent method:
Figure BDA0001765960240000141
and 5, updating the discriminator network D by a small batch random gradient descent method:
Figure BDA0001765960240000142
and 6, updating a generator network G by a small batch random gradient descent method:
Figure BDA0001765960240000143
step 7, repeating the steps 2 to 6 until the iteration number k is reached;
step 8, outputting the weight theta of the generator network G after trainingGWeight theta of discriminator network DDAnd the weight θ of the sketch fitter network SS。
And 7, generating the SAR image block by using the generator network.
Inputting the sketch block in the test data set in the paired data set of the sketch block-SAR image block constructed in the step 4 and 8-dimensional Gaussian noise into the generator network obtained by training in the step 6 to obtain a generated SAR image block.
The effect of the present invention will be further described with reference to the simulation diagram.
1. Simulation conditions are as follows:
the hardware platform for simulation of the invention is as follows: HP Z840; the software platform is as follows: PyTorch; synthetic aperture radar SAR images used in the simulation of the invention are as follows: and (3) a pyramides graph with the resolution of the Terras SAR X band being 1 meter.
2. Simulation content and results:
the method is used for carrying out experiments under the simulation condition, 70% of paired samples (accounting for 70% of total number of all samples of 5200 and about 3600 paired samples) are selected from a paired data set of 'sketch block-SAR image block' to carry out alternate training on a sketch fitter network, a discriminator network and a generator network in sequence, and the rest samples are used as test samples to obtain the SAR image block generation result shown in figure 4.
Looking at fig. 4 by columns, the first column from left to right is the original SAR image block, the second column is the sketch block of the original SAR image block (input to the generator network), the third column is the generated SAR image block (output of the generator network), and the fourth column is the fitted sketch block that generates the SAR image block (output of the sketch fitter network).
As can be seen from fig. 4: the generated SAR image block has clear edges and rich details, has more difference with the original SAR image block, and meanwhile, the comparison of the sketch fitting graph of the generated SAR image block and the sketch graph of the original SAR image block can find that the two are similar in structure, thereby ensuring the structural consistency of the generated SAR image block and the original SAR image block.
In conclusion, the method uses the generation countermeasure network of the sketch information and the structural constraint, can learn the generation of the extremely inhomogeneous region of the SAR image without supervision, and simultaneously, the two structural constraints effectively solve the problems of fuzzy and serious deformation of the image structure generated by the generation countermeasure network GAN, namely the method has good modeling capacity on the SAR image in structure and surface, can generate the SAR image sample which is consistent with the ground object structure of the original SAR image according to the sketch, and solves the problem of sample imbalance in the SAR image semantic segmentation.

Claims (8)

1. A SAR image sample generation method based on sketch and structure generation countermeasure network is characterized by comprising the following steps:
step 1, performing sketch processing on an original SAR image I to obtain a sketch I of the SAR image Is；
Step 2, sketch I in the step 1sCarrying out regionalization to obtain a regional image, and extracting an extremely heterogeneous region set of an original SAR image I from the regional image;
step 3, constructing a paired data set in the form of a sketch block-SAR image block according to the extremely heterogeneous region set of the original SAR image I obtained in the step 2;
step 4, randomly selecting 70% of data samples as a training data set and the rest data samples as a testing data set in the data set constructed in the step 3;
step 5, constructing a generation countermeasure network of sketch information and structural constraint, wherein the generation countermeasure network comprises a generator network, a discriminator network and a sketch fitter network;
step 6, training the generated countermeasure network constructed in the step 5 by using the training data set in the step 4;
step 7, inputting the test data set in the step 4 into the generated countermeasure network obtained by training in the step 6 to obtain a generated SAR image block;
in the step 2, the specific method for extracting the extremely heterogeneous region set of the original SAR image I is as follows:
firstly, the sketch I obtained in the step 1 is regionalized by sketch linesCarrying out regionalization treatment to obtain a regional image of the original SAR image, wherein the regional image comprises an aggregation region, a sketch-free region and a structural region;
secondly, mapping the regional image into an original SAR image I to obtain a mixed pixel subspace, a homogeneous pixel subspace and a structural pixel subspace of the SAR image I;
finally, extracting a mixed pixel subspace of the SAR image I as an extremely heterogeneous region set of the original SAR image I, wherein the mixed pixel subspace is composed of a plurality of extremely heterogeneous regions which are not communicated with each other and have different sizes;
in step 5, the generator network comprises an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a first maximum pooling layer, a fifth convolution layer, a sixth convolution layer, a second maximum pooling layer, a first deconvolution layer, a second deconvolution layer, a third deconvolution layer, a seventh convolution layer and an output layer which are connected in sequence; wherein, the input of the generator network is 8-dimensional Gaussian random noise z and a sketch block x in a training set, and the output is the SAR image block
Figure FDA0003265857140000021
The discriminator network comprises an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, a sixth convolution layer, a seventh convolution layer and a classifier which are connected in sequence; wherein, the input of the discriminator network is SAR image block y in the training set and the generated SAR image block
Figure FDA0003265857140000022
The output is a single node which represents the probability that the SAR image block conforms to the distribution of the real SAR samples;
the sketch fitter network comprises an input layer, a first convolution layer, a second convolution layer, a third convolution layer, a first deconvolution layer, a second deconvolution layer, a fourth convolution layer and an output layer which are connected in sequence; wherein, the input of the sketch fitting device network is SAR image block y in the training set and SAR image block generation
Figure FDA0003265857140000023
The output is a fitted sketch block of the SAR image.
2. The method for generating the SAR image sample based on the sketch and the structure generation countermeasure network according to claim 1, wherein in step 1, the specific method for performing the sketch processing on the original SAR image I is as follows:
obtaining a sketch model of the original SAR image according to the distribution characteristics of the original SAR image I, and performing sketch processing on the original SAR image I by using the sketch model to obtain a sketch I of the original SAR image Is。
3. The method for generating SAR image samples based on sketch and structure generation countermeasure network as claimed in claim 1, wherein in step 3, the specific steps of constructing the paired data sets in the form of "sketch block-SAR image block" are as follows:
1, selecting a very inhomogeneous region from the very inhomogeneous regions obtained in the step 2, wherein the very inhomogeneous region refers to any one of 1/3 extremely inhomogeneous regions with the area smaller than the area of the maximum very inhomogeneous region in the very inhomogeneous region set; then, carrying out interval dicing on the extremely inhomogeneous region by using a rectangular window with the size of 128 multiplied by 128, wherein the cutting interval size is 32, and then obtaining a plurality of cut SAR image blocks y;
step 2, according to the position of the SAR image block y in the original SAR image I, obtaining a sketch I from the step 1sAnd extracting a sketch block x corresponding to the position of the SAR image block y, and constructing a paired data set in the form of a sketch block-SAR image block.
4. The SAR image sample generation method based on sketch and structure generation countermeasure network as claimed in claim 1, characterized in that in step 6, the specific method for training the constructed generation countermeasure network is:
first, a sketch line loss function L is constructedSAntagonistic loss function LGANAnd a generator loss function L;
secondly, by the line loss function LSAntagonistic loss function LGANAnd a generator loss function L and the training data set obtained in the step 4 and the 8-dimensional Gaussian noise vector generated randomly are combined with a small batch random gradient descent method to alternately train the sketch fitter network, the discriminator network and the generator network in sequence, and finally the weight of the trained sketch fitter network, the discriminator network and the generator network is obtained.
5. The SAR image sample generation method based on sketch and structure generation countermeasure network as claimed in claim 4, wherein the generator loss function L is a geometric structure window loss function LWSketch line loss function LSAnd a penalty function LGANThe weighted sum yields:
Figure FDA0003265857140000031
wherein, thetaGRepresenting the parameters of the generator G, thetaDRepresenting parameters of a discriminator D, wherein alpha, beta and gamma are all parameters; specifically, the method comprises the following steps:
geometric window loss function LWExpressed as:
Figure FDA0003265857140000032
wherein, thetaGParameters representing generator G, pz、pXAnd pYRespectively representing the distribution of noise z, sketch blocks x in the training set and SAR image blocks y in the training set, G (z |) representing a SAR image generated conditioned on the original SAR image sketch blocks, | representing element-by-element multiplication, | · |1L representing a vector1Norm, E [. C]Expressing an average value; n is a radical ofxThe number of line segments to be sketched;
Figure FDA0003265857140000033
and representing a binary segmentation mask matrix corresponding to the ith sketch block.
6. The SAR image sample generation method based on sketch and structure generation countermeasure network as claimed in claim 5, characterized in that, the geometric structure window loss function LWThe design method specifically comprises the following steps:
step 1, constructing a geometric structure window by utilizing any sketch line segment in a sketch block x in a training set, wherein the geometric structure window surrounds the sketch line segment, an area surrounded by the geometric structure window is defined as a structure area, and an area outside the window is defined as a non-structure area;
step 2, obtaining N of SAR image block y in the training set from the geometric structure window in step 1xBinary split mask matrix
Figure FDA0003265857140000041
Wherein the content of the first and second substances,
Figure FDA0003265857140000042
the matrix size of (a) is the same as that of the SAR image block x, the value of the structural area of the SAR image block x is 1, and the value of the non-structural area of the SAR image block x is 0;
step 3, binary division mask matrix M obtained from step 2xTo obtain a geometric structure window loss function LWThe mathematical expression of (a):
Figure FDA0003265857140000043
wherein, thetaGParameters representing generator G, pz、pXAnd pYRespectively representing the distribution of 8-dimensional Gaussian noise z, the sketch block x in the training set and the SAR image block y in the training set, G (z | x) representing the SAR image block generated conditioned on the sketch block x in the training set, | indicating element-by-element multiplication, | | > |1L representing a vector1Norm, E [. C]Indicating averaging.
7. The SAR image sample generation method based on sketch and structure generation confrontation network as claimed in claim 5, wherein the sketch line loss function L used in training the generatorSThe design method specifically comprises the following steps:
sketch line loss function LSIs expressed as a sketch block x of the original SAR image and a fitting sketch block for generating the SAR image
Figure FDA0003265857140000047
Weighted L2Loss:
Figure FDA0003265857140000044
wherein, thetaGParameters representing generator G, pzAnd pXRespectively representing the distribution of 8-dimensional Gaussian noise z and the sketch block x in the training set, E [ ·]Indicating mean, y+And y-Respectively representing the inside part and the outside part of a geometric structure window in the SAR image block y, xiAnd
Figure FDA0003265857140000045
respectively representing original SAR image sketch block x and generating SAR image fitting sketch block
Figure FDA0003265857140000046
A value at a specified location i;
setting a sketch block x in a training set, and expressing the ratio lambda of the number of pixels of a sketch part of an SAR image block y in the training set to the total number of pixels of the image as follows:
Figure FDA0003265857140000051
setting a sample x of a sketch block of the original SAR image, 8-dimensional Gaussian random noise z, a generator G and a sketch fitting device S, and generating a fitting sketch block of the SAR image
Figure FDA0003265857140000052
Can be expressed as:
Figure FDA0003265857140000053
sketch loss function L 'used in training sketch fitter'SThe design method is as follows:
Figure FDA0003265857140000054
wherein, thetaSParameters, p, representing a sketch fitter SXAnd pYRespectively representing the distribution of the sketch blocks x and the SAR image blocks y in the training set, E [ ·]Indicating mean, y+And y-Respectively representing the inside part and the outside part of a geometric structure window in the SAR image block y, xiAnd
Figure FDA0003265857140000055
respectively representing original SAR image sketch block x and generating SAR image fitting sketch block
Figure FDA0003265857140000056
The definition of the value, λ, at the given position i is the sketch line loss function L used in training the generatorSThe design method of (1).
8. The SAR image sample generation method based on sketch and structure generation countermeasure network as claimed in claim 5, characterized in that the countermeasure loss function LGANThe design method specifically comprises the following steps:
the input of the discriminator D is SAR image block y in the training set and the generated SAR image block
Figure FDA0003265857140000057
Outputting the probability that the sample accords with the original SAR image distribution; wherein, according to the definition of the condition GAN, the loss resisting function LGANThe mathematical expression of (a) is:
Figure FDA0003265857140000058
wherein, thetaGRepresenting the parameters of the generator G, thetaDParameter, p, representing discriminator Dz、pXAnd pYRespectively representing the distribution of 8-dimensional Gaussian noise z, a sketch block x in a training set and an SAR image block y in the training set; g (z | x) represents a SAR image block generated conditioned on a sketch block x in the training set
Figure FDA0003265857140000059
D (y | x) represents the output of the discriminator when inputting SAR image block y in the training set, and D (G (z | x)) represents the input of the discriminator to generate SAR image block
Figure FDA00032658571400000510
An output of time; e [. C]Indicating averaging.
CN201810928531.5A 2018-08-15 2018-08-15 SAR image sample generation method based on sketch and structure generation countermeasure network Active CN109190684B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810928531.5A CN109190684B (en) 2018-08-15 2018-08-15 SAR image sample generation method based on sketch and structure generation countermeasure network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810928531.5A CN109190684B (en) 2018-08-15 2018-08-15 SAR image sample generation method based on sketch and structure generation countermeasure network

Publications (2)

Publication Number Publication Date
CN109190684A CN109190684A (en) 2019-01-11
CN109190684B true CN109190684B (en) 2022-03-04

Family

ID=64935926

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810928531.5A Active CN109190684B (en) 2018-08-15 2018-08-15 SAR image sample generation method based on sketch and structure generation countermeasure network

Country Status (1)

Country Link
CN (1) CN109190684B (en)

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109903242A (en) * 2019-02-01 2019-06-18 深兰科技（上海）有限公司 A kind of image generating method and device
CN109871898B (en) * 2019-02-27 2020-04-07 南京中设航空科技发展有限公司 Method for generating deposit training sample by using generated confrontation network
CN109816614A (en) * 2019-02-28 2019-05-28 乐山知行智能科技有限公司 Synthetic method, device and the storage medium of image
CN110097059B (en) * 2019-03-22 2021-04-02 中国科学院自动化研究所 Document image binarization method, system and device based on generation countermeasure network
CN109978893B (en) * 2019-03-26 2023-06-20 腾讯科技（深圳）有限公司 Training method, device, equipment and storage medium of image semantic segmentation network
CN110096994B (en) * 2019-04-28 2021-07-23 西安电子科技大学 Small sample PolSAR image classification method based on fuzzy label semantic prior
CN110210486B (en) * 2019-05-15 2021-01-01 西安电子科技大学 Sketch annotation information-based generation countermeasure transfer learning method
CN110298384B (en) * 2019-06-03 2021-03-12 西华大学 Countermeasure sample image generation method and apparatus
CN110210418B (en) * 2019-06-05 2021-07-23 西安电子科技大学 SAR image airplane target detection method based on information interaction and transfer learning
CN110197517B (en) * 2019-06-11 2023-01-31 常熟理工学院 SAR image coloring method based on multi-domain cycle consistency countermeasure generation network
CN110555811A (en) * 2019-07-02 2019-12-10 五邑大学 SAR image data enhancement method and device and storage medium
CN110610124B (en) * 2019-07-30 2021-11-30 珠海亿智电子科技有限公司 Image generation method based on generation countermeasure network
CN111027292B (en) * 2019-11-29 2021-05-28 北京邮电大学 Method and system for generating limited sampling text sequence
CN111951187B (en) * 2020-07-21 2023-04-18 电子科技大学 SAR image enhancement method based on transformation gradient domain
CN112560795B (en) * 2020-12-30 2022-07-26 南昌航空大学 SAR image target recognition algorithm based on CN-GAN and CNN
CN112766381B (en) * 2021-01-22 2023-01-24 西安电子科技大学 Attribute-guided SAR image generation method under limited sample
CN115374859A (en) * 2022-08-24 2022-11-22 东北大学 Method for classifying unbalanced and multi-class complex industrial data

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106611420A (en) * 2016-12-30 2017-05-03 西安电子科技大学 SAR image segmentation method based on deconvolution network and sketch direction constraint
CN106611422A (en) * 2016-12-30 2017-05-03 西安电子科技大学 Stochastic gradient Bayesian SAR image segmentation method based on sketch structure
CN107341813A (en) * 2017-06-15 2017-11-10 西安电子科技大学 SAR image segmentation method based on structure learning and sketch characteristic inference network
CN107563428A (en) * 2017-08-25 2018-01-09 西安电子科技大学 Classification of Polarimetric SAR Image method based on generation confrontation network
CN107886491A (en) * 2017-11-27 2018-04-06 深圳市唯特视科技有限公司 A kind of image combining method based on pixel arest neighbors
CN108399625A (en) * 2018-02-28 2018-08-14 电子科技大学 A kind of SAR image orientation generation method generating confrontation network based on depth convolution

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106611420A (en) * 2016-12-30 2017-05-03 西安电子科技大学 SAR image segmentation method based on deconvolution network and sketch direction constraint
CN106611422A (en) * 2016-12-30 2017-05-03 西安电子科技大学 Stochastic gradient Bayesian SAR image segmentation method based on sketch structure
CN107341813A (en) * 2017-06-15 2017-11-10 西安电子科技大学 SAR image segmentation method based on structure learning and sketch characteristic inference network
CN107563428A (en) * 2017-08-25 2018-01-09 西安电子科技大学 Classification of Polarimetric SAR Image method based on generation confrontation network
CN107886491A (en) * 2017-11-27 2018-04-06 深圳市唯特视科技有限公司 A kind of image combining method based on pixel arest neighbors
CN108399625A (en) * 2018-02-28 2018-08-14 电子科技大学 A kind of SAR image orientation generation method generating confrontation network based on depth convolution

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
Generative adversarial network-based restoration of speckled SAR images;Puyang Wang等;《2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)》;20180312;第1-5页 *
Local Maximal Homogeneous Region Search for SAR Speckle Reduction With Sketch-Based Geometrical Kernel Function;Jie Wu等;《IEEE Transactions on Geoscience and Remote Sensing》;20140106;第52卷(第9期);第5751-5764页 *
基于深度学习和层次语义模型的极化SAR分类;石俊飞等;《自动化学报》;20170215;第43卷(第2期);第215-226页 *
基于深度学习网络的SAR图像目标识别研究;赵菲妮;《中国优秀硕士学位论文全文数据库 (信息科技辑)》;20180415;第I136-2394页 *
基于素描稀疏表示和低秩分解的SAR图像目标检测;闫晓莉;《中国优秀硕士学位论文全文数据库 (信息科技辑)》;20170315;第I136-2215页 *

Also Published As

Publication number Publication date
CN109190684A (en) 2019-01-11

Similar Documents

Publication Publication Date Title
CN109190684B (en) SAR image sample generation method based on sketch and structure generation countermeasure network
CN110135267B (en) Large-scene SAR image fine target detection method
CN113159051B (en) Remote sensing image lightweight semantic segmentation method based on edge decoupling
CN109035142B (en) Satellite image super-resolution method combining countermeasure network with aerial image prior
CN108154192B (en) High-resolution SAR terrain classification method based on multi-scale convolution and feature fusion
CN107798381B (en) Image identification method based on convolutional neural network
CN110136154B (en) Remote sensing image semantic segmentation method based on full convolution network and morphological processing
CN111612807B (en) Small target image segmentation method based on scale and edge information
CN106228185B (en) A kind of general image classifying and identifying system neural network based and method
CN111563902A (en) Lung lobe segmentation method and system based on three-dimensional convolutional neural network
CN111310666B (en) High-resolution image ground feature identification and segmentation method based on texture features
CN111612017B (en) Target detection method based on information enhancement
CN111832501A (en) Remote sensing image text intelligent description method for satellite on-orbit application
CN110929602A (en) Foundation cloud picture cloud shape identification method based on convolutional neural network
CN111369442B (en) Remote sensing image super-resolution reconstruction method based on fuzzy kernel classification and attention mechanism
CN107808138B (en) Communication signal identification method based on FasterR-CNN
CN110598806A (en) Handwritten digit generation method for generating countermeasure network based on parameter optimization
CN104866868A (en) Metal coin identification method based on deep neural network and apparatus thereof
CN111368935B (en) SAR time-sensitive target sample amplification method based on generation countermeasure network
CN111275640B (en) Image enhancement method for fusing two-dimensional discrete wavelet transform and generation of countermeasure network
CN112233129B (en) Deep learning-based parallel multi-scale attention mechanism semantic segmentation method and device
CN107967474A (en) A kind of sea-surface target conspicuousness detection method based on convolutional neural networks
CN113095333B (en) Unsupervised feature point detection method and unsupervised feature point detection device
CN112950780B (en) Intelligent network map generation method and system based on remote sensing image
CN113743417B (en) Semantic segmentation method and semantic segmentation device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant