CN106203489B - Classification of Polarimetric SAR Image method based on multiple dimensioned depth direction wave network - Google Patents
Classification of Polarimetric SAR Image method based on multiple dimensioned depth direction wave network Download PDFInfo
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Abstract
The invention discloses a kind of Classification of Polarimetric SAR Image method based on multiple dimensioned depth direction wave network, this method realizes step are as follows: (1) inputs polarimetric SAR image;(2) Pauli characteristics of decomposition is extracted;(3) training sample eigenmatrix is constructed;(4) multiple dimensioned depth direction wave network is initialized;(5) the multiple dimensioned depth direction wave network of training;(6) test sample eigenmatrix is constructed;(7) category of test sample is obtained;(8) nicety of grading is calculated;(9) it paints;(10) polarimetric SAR image after output colouring.The present invention classifies to polarimetric SAR image using the direction wave filter of different scale as the filter of multiple dimensioned depth direction wave network, so that the present invention has the advantages that retain the direction character and global characteristics of polarimetric SAR image well.
Description
Technical field
The invention belongs to technical field of image processing, further relate to polarization synthetic aperture radar image sorting technique neck
Polarimetric synthetic aperture radar SAR (Synthetic Aperture of one of the domain based on multiple dimensioned depth direction wave network
Radar) image classification method.The present invention can be used for classifying to the atural object of polarimetric SAR image, can effectively improve polarization
The precision of SAR image classification.
Background technique
Synthetic aperture radar is a kind of high-resolution imaging radar.Since microwave has through characteristic, not by light intensity
It influences, therefore synthetic aperture radar has round-the-clock, round-the-clock ability to work.With the development of technology, synthetic aperture radar
Gradually develop to the direction of high-resolution, multipolarization, multichannel.Compared to traditional single polarization SAR, multipolarization SAR is capable of providing
Target information more abundant is conducive to determine and understand scattering mechanism, improves the ability of target detection and Classification and Identification.
Zhongshan University is in a kind of patent " POLSAR image unsupervised segmentation side identified based on target scattering of its application
It proposes and a kind of is reflected in method " (number of patent application: 201210222987.2, publication number: CN102799896A) based on target scattering
The method of other POLSAR image unsupervised segmentation.This method calculates POLSAR image Polarization scattering entropy first and surface dissipates
It penetrates, the Similarity Parameter of even scattering and volume scattering, and POLSAR image initial is divided into classification using these parameters;Then it selects
It takes the minimum antenna of the atural object based on surface scattering to receive power features to polarize as antenna polarization state, calculates each pixel
Antenna receive power;It finally calculates the cluster centre of every one kind and is divided all pixels again according to Polarization scattering difference measurement
Class simultaneously updates cluster centre, repeats this process until cluster centre is no longer changed.This method belongs to unsupervised classification
Method, Terrain Scattering can accurately be described by having, and can correspond to practical scattering situation very well, reduce the fortune of classification adjustment
The advantages that evaluation time, still, the shortcoming that this method still has is, since this method belongs to unsupervised segmentation, Zhi Nengyi
Classify by scattered information to atural object, so that classification accuracy is relatively low.
Patent " classification method based on polarimetric SAR image that denoising certainly encode " of the Xian Electronics Science and Technology University in its application
Proposed in (number of patent application: 201510108639.6, publication number: CN104751172A) it is a kind of based on denoising from the pole of coding
Change the classification method of SAR image.This method inputs a polarimetric SAR image to be sorted first, extracts the polarimetric SAR image
Primitive character and its neighborhood characteristics, then take logarithm process to primitive character and neighborhood characteristics, its noise is made to meet Gauss point
Secondly cloth determines the number of plies of denoising autocoding network, each node layer and data noise and training denoising autocoding network,
Then classified using the polarimetric SAR image of trained denoising autocoding network handles classification.The process employs go
Autocoding of making an uproar network simplifies the process of feature extraction, improves the generalization ability of feature and the nicety of grading to image, but
It is that the shortcoming that this method still has is, since this method is extracted the neighborhood characteristics of polarimetric SAR image, can not to retain
The global characteristics of polarimetric SAR image.
Summary of the invention
It is a kind of based on multiple dimensioned depth direction wave it is an object of the invention in view of the above shortcomings of the prior art, propose
The Classification of Polarimetric SAR Image method of network.The present invention can improve the nicety of grading of polarimetric SAR image, while can have well
The advantages of retaining the global characteristics of polarimetric SAR image.
Realize that the specific steps of the present invention are as follows:
(1) coherence matrix of a polarimetric SAR image to be sorted is inputted;
(2) Pauli Pauli characteristics of decomposition value is extracted:
(2a) uses Pauli Pauli decomposition formula, extracts Pauli from each pixel of polarimetric SAR image to be sorted
The a that Pauli is decomposed, tri- characteristic values of b, c;
(2b) will extract a of Pauli Pauli decomposition, tri- spies of b, c from each pixel of polarimetric SAR image to be sorted
Value indicative is normalized to respectively between [0,255];
(3) eigenmatrix of training sample is constructed:
(3a) randomly selects 20000 pixels as training sample from every type objects of polarimetric SAR image to be sorted
This;
(3b) chooses the square of pericentral 21 × 21 size centered on each pixel in training sample
All pixels point in neighborhood, a that the Pauli Pauli that each pixel in the square area is extracted is decomposed, b, c tri-
Characteristic value forms the eigenmatrix of 21 × 21 × 3 sizes of each pixel in training sample;
(4) multiple dimensioned depth direction wave network is initialized:
According to the following formula, the matrix of n × n size is randomly generated as initial filter group in (4a):
Wherein, x indicates initial filter group, and rand indicates the operation that matrix is randomly generated, and n indicates the different rulers of filter
Degree takes 3,4,6, * expression multiplication operations, and sqrt indicates evolution operation, and f indicates filter-bank parameters,L table
Show the number of plies of multiple dimensioned depth direction wave network.
(4b) is transformed into Gaussian filter group according to the following formula, by initial filter group:
Wherein, y indicates Gaussian filter group, and x indicates initial filter group, and e indicates to grasp by the index of the nature truth of a matter of e
Make.
Gaussian filter group according to the following formula, is rotated different angles by (4c) counterclockwise, obtains direction wave filter group:
Wherein, z indicates direction wave filter group, and rot0 indicates 0 degree of rotation counterclockwise, and rot90 indicates rotation 90 counterclockwise
Degree, rotation 180 degree that rot180 expression is counterclockwise, % expression remainder operation, the serial number of i expression Gaussian filter group, i=1,2,
The sum of 3 ..., M, M expression Gaussian filter group.
Direction wave filter group is input in multiple dimensioned depth direction wave network by (4d), and what is initialized is multiple dimensioned
Depth direction wave network;
(5) the multiple dimensioned depth direction wave network of training:
The eigenmatrix of 21 × 21 × 3 sizes of each pixel in training sample is input to the more of initialization by (5a)
In scale depth direction wave network;
The multiple dimensioned depth direction wave network of (5b) training initialization, obtains trained multiple dimensioned depth direction wave net
Network;
(6) eigenmatrix of test sample is constructed:
(6a) chooses all pixels point as test sample from every type objects of polarimetric SAR image to be sorted;
(6b) chooses the square of pericentral 21 × 21 size centered on each pixel in test sample
All pixels point in region, a that the Pauli Pauli that each pixel in the square area is extracted is decomposed, b, c tri-
Characteristic value forms the eigenmatrix of 21 × 21 × 3 sizes of each pixel in test sample;
(7) category of each pixel in test sample is obtained:
The eigenmatrix of 21 × 21 × 3 sizes of each pixel in test sample is input to trained more rulers
It spends in depth direction wave network, obtains the category of each pixel in test sample;
(8) nicety of grading of polarimetric SAR image is calculated:
The category of pixel each into test sample is compared with real-world object category, by the consistent pixel of category
Nicety of grading of the ratio of pixel number as polarimetric SAR image in point number and test sample;
(9) it paints:
The category of sorted polarimetric SAR image pixel is arranged in and polarimetric SAR image size to be sorted by (9a)
The label matrix is expressed as piece image, obtains sorted polarimetric SAR image by equal label matrix;
In the polarimetric SAR image of (9b) after sorting, using red, green, blue three colors as three primary colours, according to three
Primary colours colouring principle is painted, the polarimetric SAR image after being painted;
(10) polarimetric SAR image after output colouring.
The present invention compared with prior art, has the advantage that
First, due to classifying to polarimetric SAR image, overcoming present invention employs multiple dimensioned depth direction wave network
It can only classify in the prior art by scattered information to atural object, so that problem that classification accuracy is relatively low, so that this hair
It is bright that there is the advantages of further feature for extracting polarimetric SAR image, raising nicety of grading.
Second, since direction wave filter group is input in multiple dimensioned depth direction wave network by the present invention, to polarization
SAR image is classified, and is overcome the neighborhood characteristics that polarimetric SAR image is directly utilized in the prior art, can not be retained polarization
The problem of global characteristics of SAR image, so that the present invention has the advantages that retain the global characteristics of polarimetric SAR image well.
Detailed description of the invention
Fig. 1 is flow chart of the invention;
Fig. 2 is analogous diagram of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawing.
Referring to Fig.1, the step of present invention realizes is as follows:
Step 1, the coherence matrix of a polarimetric SAR image to be sorted is inputted.
Step 2, Pauli Pauli characteristics of decomposition value is extracted.
Using Pauli Pauli decomposition formula, Pauli Pauli is extracted from each pixel of polarimetric SAR image to be sorted
The a of decomposition, b, tri- characteristic values of c.
Pauli Pauli decomposition formula is as follows:
Wherein, a indicates that the scattering energy of the odd times scattering of each pixel in polarimetric SAR image to be sorted, b indicate
The scattering energy of the even scattering of each pixel in polarimetric SAR image to be sorted, c indicate polarization SAR figure to be sorted
The scattering energy of the degree angle even scattering of each pixel as in, T (1,1) indicate the relevant of polarimetric SAR image to be sorted
The element of matrix the first row first row, T (2,2) indicate coherence matrix the second row secondary series of polarimetric SAR image to be sorted
Element, T (3,3) indicate the tertial element of coherence matrix the third line of polarimetric SAR image to be sorted.
The a of Pauli Pauli decomposition, tri- characteristic values of b, c will be extracted from each pixel of polarimetric SAR image to be sorted
It is normalized between [0,255] respectively.
Step 3, the eigenmatrix of training sample is constructed.
20000 pixels are randomly selected from every type objects of polarimetric SAR image to be sorted as training sample.
Centered on each pixel in training sample, the square area of pericentral 21 × 21 size is chosen
In all pixels point, by the square area each pixel extract Pauli Pauli decompose a, tri- features of b, c
Value forms the eigenmatrix of 21 × 21 × 3 sizes of each pixel in training sample.
Step 4, multiple dimensioned depth direction wave network is initialized.
According to the following formula, the matrix of n × n size is randomly generated as initial filter group:
Wherein, x indicates initial filter group, and rand indicates the operation that matrix is randomly generated, and n indicates the different rulers of filter
Degree takes 3,4,6, * expression multiplication operations, and sqrt indicates evolution operation, and f indicates filter-bank parameters,L table
Show the number of plies of multiple dimensioned depth direction wave network.
According to the following formula, initial filter group is transformed into Gaussian filter group:
Wherein, y indicates Gaussian filter group, and x indicates initial filter group, and e indicates to grasp by the index of the nature truth of a matter of e
Make.
According to the following formula, Gaussian filter group is rotated to different angles counterclockwise, obtains direction wave filter group:
Wherein, z indicates direction wave filter group, and rot0 indicates 0 degree of rotation counterclockwise, and rot90 indicates rotation 90 counterclockwise
Degree, rotation 180 degree that rot180 expression is counterclockwise, % expression remainder operation, the serial number of i expression Gaussian filter group, i=1,2,
The sum of 3 ..., M, M expression Gaussian filter group.
Direction wave filter group is input in multiple dimensioned depth direction wave network, the multiple dimensioned depth initialized
Direction wave network.
Multiple dimensioned depth direction wave network is formed by 7 layers, and the 1st layer is input layer, and the 2nd layer and the 4th layer filters for direction wave
The convolutional layer of device group composition, the 3rd layer and the 5th layer is down-sampling layer, and the 6th layer is full articulamentum, and the 7th layer is linear regression classifier
softmax。
Step 5, the multiple dimensioned depth direction wave network of training.
The eigenmatrix of 21 × 21 × 3 sizes of each pixel in training sample is input to the multiple dimensioned of initialization
In depth direction wave network.
The multiple dimensioned depth direction wave network of training initialization, obtains trained multiple dimensioned depth direction wave network.
Specific step is as follows for the multiple dimensioned depth direction wave network of training initialization:
The first step, using the eigenmatrix of each pixel of training sample as the input of multiple dimensioned depth direction wave network
The input of layer obtains the output category of the output layer of multiple dimensioned depth direction wave network by propagated forward.
Second step, by the object in the output category and polarimetric SAR image of the output layer of multiple dimensioned depth direction wave network
The mean square error of category is as training error.
Third step is minimized training error, is obtained trained multiple dimensioned depth direction wave net using back-propagation algorithm
Network.
Step 6, the eigenmatrix of test sample is constructed.
All pixels point is chosen from every type objects of polarimetric SAR image to be sorted as test sample.
Centered on each pixel in test sample, the square area of pericentral 21 × 21 size is chosen
In all pixels point, by the square area each pixel extract Pauli Pauli decompose a, tri- features of b, c
Value forms the eigenmatrix of 21 × 21 × 3 sizes of each pixel in test sample.
Step 7, the category of each pixel in test sample is obtained.
It is trained more by the eigenmatrix of 21 × 21 × 3 sizes of each pixel in test sample to be input to
In scale depth direction wave network, the category of each pixel in test sample is obtained.
Step 8, the nicety of grading of polarimetric SAR image is calculated.
The category of pixel each into test sample is compared with real-world object category, by the consistent pixel of category
Nicety of grading of the ratio of pixel number as polarimetric SAR image in point number and test sample.
Step 9, it paints.
The category of sorted polarimetric SAR image pixel is arranged in equal in magnitude with polarimetric SAR image to be sorted
Label matrix, which is expressed as piece image, obtains sorted polarimetric SAR image.
In polarimetric SAR image after sorting, using red, green, blue three colors as three primary colours, according to three primary colours
Colouring principle is painted, the polarimetric SAR image after being painted.
Step 10, the polarimetric SAR image after output colouring.
Effect of the present invention is described further below with reference to analogous diagram:
1, emulation experiment condition:
Emulation experiment of the invention is Six-Core AMD Opteron (tm) Processor in dominant frequency 2.8GHz
Realization is programmed in the software environment of 2439SE, the hardware environment of memory 32GB and MATLAB R2012b.
2, analysis of simulation result:
Fig. 2 is analogous diagram of the invention, wherein Fig. 2 (a) is polarimetric SAR image used in emulation experiment of the present invention, should
Image is San Francisco San Francisco that the AIRSAR system of NASA Jet Propulsion Laboratory (NASA/JPL) obtains
The data in area are located at L-band, are the full polarimetric SAR data of one four view, size 1800*1380.The region is comprising 5 classes
Object: high density city (High-Density Urban), low-density city (Low-Density Urban), waters (Water),
Vegetation (Vegetation) and development zone (Developed).Fig. 2 (b) is the support vector machines classification side using the prior art
The simulation result diagram of method;Fig. 2 (c) is the result figure using the sparse svm classifier method of the prior art;Fig. 2 (d) is using existing
The result figure of the depth S VM classification method of technology;Fig. 2 (e) is the result figure using the self-encoding encoder classification method of the prior art;
Fig. 2 (f) is the result figure using the convolutional neural networks CNN classification method of the prior art;Fig. 2 (g) is emulation knot of the invention
Fruit figure.
Polarization synthetic aperture radar image to be sorted is divided into 5 classes by emulation experiment of the invention.
Fig. 2 (b), Fig. 2 (c), Fig. 2 (d), Fig. 2 (e), Fig. 2 (f) and Fig. 2 (g) are compared respectively, it can be seen that using this
The method of invention, compared to the support vector machines classification method using the prior art, the sparse SVM using the prior art points
Class method, the depth S VM classification method using the prior art, the self-encoding encoder classification method using the prior art and use are existing
The convolutional neural networks CNN classification method of technology, wrong point of miscellaneous point is less in region, improves nicety of grading.
Using the support vector machines classification method of the prior art, using the prior art sparse svm classifier method, adopt
With the depth S VM classification method of the prior art, the self-encoding encoder classification method using the prior art and the volume using the prior art
Product neural network CNN classification method and the method for the present invention count positive precision of classifying, and the results are shown in Table 1.
From table 1 it follows that not only having on mean accuracy larger with the method for the present invention compared to other five kinds of methods
Raising, be also improved largely in every class precision, this be primarily due to the present invention have retain polarimetric SAR image well
Directional information the advantages of, to improve the computational efficiency of image classification.
The nicety of grading that 1. 6 kinds of methods of table obtain in simulations
Atural object classification | SVM | Sparse SVM | Depth S VM | Self-encoding encoder | CNN | The present invention |
High density city | 99.78% | 99.54% | 99.42% | 99.13% | 99.67% | 100% |
Low-density city | 84.11% | 79.57% | 84.78% | 86.32% | 90.11% | 96.21% |
Waters | 68.44% | 70.99% | 68.98% | 79.01% | 90.34% | 95.56% |
Vegetation | 67.23% | 78.46% | 68.05% | 61.92% | 90.65% | 93.24% |
Development zone | 71.48% | 76.88% | 69.05% | 68.22% | 95.29% | 95.81% |
Mean accuracy | 85.25% | 87.04% | 85.29% | 85.74% | 95.02% | 97.46% |
Claims (2)
1. a kind of Classification of Polarimetric SAR Image method based on multiple dimensioned depth direction wave network, includes the following steps:
(1) coherence matrix of a polarimetric SAR image to be sorted is inputted;
(2) Pauli Pauli characteristics of decomposition value is extracted:
(2a) uses Pauli Pauli decomposition formula, and Pauli Pauli is extracted from each pixel of polarimetric SAR image to be sorted
The a of decomposition, b, tri- characteristic values of c;
(2b) will extract a of Pauli Pauli decomposition, tri- characteristic values of b, c from each pixel of polarimetric SAR image to be sorted
It is normalized between [0,255] respectively;
(3) eigenmatrix of training sample is constructed:
(3a) randomly selects 20000 pixels as training sample from every class ground object target of polarimetric SAR image to be sorted
This;
(3b) chooses the square area of pericentral 21 × 21 size centered on each pixel in training sample
In all pixels point, by the square area each pixel extract Pauli Pauli decompose a, tri- features of b, c
Value forms the eigenmatrix of 21 × 21 × 3 sizes of each pixel in training sample;
(4) multiple dimensioned depth direction wave network is initialized:
According to the following formula, the matrix of n × n size is randomly generated as initial filter group in (4a):
Wherein, x indicates initial filter group, and rand indicates the operation that matrix is randomly generated, and n indicates the different scale of filter,
3,4,6, * expression multiplication operations are taken, sqrt indicates evolution operation, and f indicates filter-bank parameters,L indicates more
The number of plies of scale depth direction wave network;
(4b) is transformed into Gaussian filter group according to the following formula, by initial filter group:
Wherein, y indicates Gaussian filter group, and x indicates initial filter group, and e is indicated using e as the index operation of the nature truth of a matter;
Gaussian filter group according to the following formula, is rotated different angles by (4c) counterclockwise, obtains direction wave filter group:
Wherein, z indicates direction wave filter group, and rot0 indicates that 0 degree of rotation counterclockwise, rot90 expression are rotated by 90 ° counterclockwise,
Rotation 180 degree, % indicate that remainder operates counterclockwise for rot180 expression, the serial number of i expression Gaussian filter group, i=1,2,
The sum of 3 ..., M, M expression Gaussian filter group;
Direction wave filter group is input in multiple dimensioned depth direction wave network by (4d), the multiple dimensioned depth initialized
Direction wave network;
The multiple dimensioned depth direction wave network is formed by 7 layers, and the 1st layer is input layer, and the 2nd layer and the 4th layer is filtered for direction wave
The convolutional layer of wave device group composition, the 3rd layer and the 5th layer is down-sampling layer, and the 6th layer is full articulamentum, and the 7th layer is classified for linear regression
Device softmax;
(5) the multiple dimensioned depth direction wave network of training:
The eigenmatrix of 21 × 21 × 3 sizes of each pixel in training sample is input to the multiple dimensioned of initialization by (5a)
In depth direction wave network;
The multiple dimensioned depth direction wave network of (5b) training initialization, obtains trained multiple dimensioned depth direction wave network;
Specific step is as follows for the multiple dimensioned depth direction wave network of the training initialization:
The first step, using the eigenmatrix of each pixel of training sample as the input layer of multiple dimensioned depth direction wave network
Input, by propagated forward, obtains the output category of the output layer of multiple dimensioned depth direction wave network;
Second step, by the object category in the output category and polarimetric SAR image of the output layer of multiple dimensioned depth direction wave network
Mean square error as training error;
Third step is minimized training error, is obtained trained multiple dimensioned depth direction wave network using back-propagation algorithm;
(6) eigenmatrix of test sample is constructed:
(6a) chooses all pixels point as test sample from every type objects of polarimetric SAR image to be sorted;
(6b) chooses the square area of pericentral 21 × 21 size centered on each pixel in test sample
In all pixels point, by the square area each pixel extract Pauli Pauli decompose a, tri- features of b, c
Value forms the eigenmatrix of 21 × 21 × 3 sizes of each pixel in test sample;
(7) category of each pixel in test sample is obtained:
The eigenmatrix of 21 × 21 × 3 sizes of each pixel in test sample is input to trained multiple dimensioned depth
It spends in the wave network of direction, obtains the category of each pixel in test sample;
(8) nicety of grading of polarimetric SAR image is calculated:
The category of pixel each into test sample is compared with real-world object category, by category consistent pixel
Several niceties of grading with the ratio of pixel number in test sample as polarimetric SAR image;
(9) it paints:
The category of sorted polarimetric SAR image pixel is arranged in equal in magnitude with polarimetric SAR image to be sorted by (9a)
Label matrix, which is expressed as piece image, obtains sorted polarimetric SAR image;
In the polarimetric SAR image of (9b) after sorting, using red, green, blue three colors as three primary colours, according to three primary colours
Colouring principle is painted, the polarimetric SAR image after being painted;
(10) polarimetric SAR image after output colouring.
2. the Classification of Polarimetric SAR Image method according to claim 1 based on multiple dimensioned depth direction wave network, feature
Be: Pauli Pauli decomposition formula described in step (2a) is as follows:
Wherein, a indicates that the scattering energy of the odd times scattering of each pixel in polarimetric SAR image to be sorted, b are indicated wait divide
The scattering energy of the even scattering of each pixel in the polarimetric SAR image of class, c are indicated in polarimetric SAR image to be sorted
Each pixel degree angle even scattering scattering energy, T (1,1) indicates the coherence matrix of polarimetric SAR image to be sorted
The element of the first row first row, T (2,2) indicate the element of coherence matrix the second row secondary series of polarimetric SAR image to be sorted,
T (3,3) indicates the tertial element of coherence matrix the third line of polarimetric SAR image to be sorted.
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CN107909084B (en) * | 2017-11-15 | 2021-07-13 | 电子科技大学 | Haze concentration prediction method based on convolution-linear regression network |
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Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103824084A (en) * | 2014-03-12 | 2014-05-28 | 西安电子科技大学 | Polarimetric SAR (Synthetic Aperture Radar) image classification method based on SDIT (Secretome-Derived Isotopic Tag) and SVM (Support Vector Machine) |
CN104077599A (en) * | 2014-07-04 | 2014-10-01 | 西安电子科技大学 | Polarization SAR image classification method based on deep neural network |
CN104156728A (en) * | 2014-07-14 | 2014-11-19 | 西安电子科技大学 | Polarized SAR image classification method based on stacked code and softmax |
CN104240249A (en) * | 2014-09-12 | 2014-12-24 | 西安电子科技大学 | SAR image change detection method based on directional wavelet transformation and improved level set |
CN104318245A (en) * | 2014-10-20 | 2015-01-28 | 西安电子科技大学 | Sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification |
CN104408467A (en) * | 2014-11-26 | 2015-03-11 | 西安电子科技大学 | Polarimetric SAR (synthetic aperture radar) image classification method based on pyramid sampling and SVM (support vector machine) |
CN104680184A (en) * | 2015-03-14 | 2015-06-03 | 西安电子科技大学 | Polarization SAR terrain classification method based on deep RPCA |
CN105718957A (en) * | 2016-01-26 | 2016-06-29 | 西安电子科技大学 | Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network |
-
2016
- 2016-07-01 CN CN201610512915.XA patent/CN106203489B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103824084A (en) * | 2014-03-12 | 2014-05-28 | 西安电子科技大学 | Polarimetric SAR (Synthetic Aperture Radar) image classification method based on SDIT (Secretome-Derived Isotopic Tag) and SVM (Support Vector Machine) |
CN104077599A (en) * | 2014-07-04 | 2014-10-01 | 西安电子科技大学 | Polarization SAR image classification method based on deep neural network |
CN104156728A (en) * | 2014-07-14 | 2014-11-19 | 西安电子科技大学 | Polarized SAR image classification method based on stacked code and softmax |
CN104240249A (en) * | 2014-09-12 | 2014-12-24 | 西安电子科技大学 | SAR image change detection method based on directional wavelet transformation and improved level set |
CN104318245A (en) * | 2014-10-20 | 2015-01-28 | 西安电子科技大学 | Sparse depth network based polarization SAR (Synthetic Aperture Radar) image classification |
CN104408467A (en) * | 2014-11-26 | 2015-03-11 | 西安电子科技大学 | Polarimetric SAR (synthetic aperture radar) image classification method based on pyramid sampling and SVM (support vector machine) |
CN104680184A (en) * | 2015-03-14 | 2015-06-03 | 西安电子科技大学 | Polarization SAR terrain classification method based on deep RPCA |
CN105718957A (en) * | 2016-01-26 | 2016-06-29 | 西安电子科技大学 | Polarized SAR image classification method based on nonsubsampled contourlet convolutional neural network |
Non-Patent Citations (1)
Title |
---|
基于多尺度卷积神经网络的图像检索算法;王利卿等;《软件导刊》;20160229;第15卷(第2期);第38-40页 |
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