CN106815601A - Hyperspectral image classification method based on recurrent neural network - Google Patents

Hyperspectral image classification method based on recurrent neural network Download PDF

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CN106815601A
CN106815601A CN201710014713.7A CN201710014713A CN106815601A CN 106815601 A CN106815601 A CN 106815601A CN 201710014713 A CN201710014713 A CN 201710014713A CN 106815601 A CN106815601 A CN 106815601A
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张向荣
焦李成
姜凯
安金梁
冯婕
李阳阳
侯彪
马文萍
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Abstract

The invention discloses a kind of hyperspectral image classification method based on recurrent neural network, mainly solution existing method input feature vector identification is weaker, and local spatial feature extracts insufficient problem, and implementation step includes：1. the spatial texture feature and rarefaction representation feature of high spectrum image are extracted, and to its stacked combination into low-level feature；2. sample local space sequence signature is extracted on low-level feature；3. recurrent neural networks model is built according to local space sequence signature, and using training sample local space sequence signature training recurrent neural networks model parameter；4. the recurrent neural networks model for the input of test sample local space sequence signature being trained, obtains the high-level semantics features of high abstraction, obtains the classification information of test sample.The present invention improves the accuracy of classification hyperspectral imagery using the method for deep learning, can be used for vegetation investigation, and disaster surveillance, cartography and information are obtained.

Description

Hyperspectral image classification method based on recurrent neural network
Technical field
The invention belongs to technical field of image processing, it is related to a kind of hyperspectral image classification method, can be used for EO-1 hyperion distant Feel the classification of image.
Background technology
At present, as the spectral resolution of remote sensor is improved constantly, people are to object spectrum attribute, the cognition of feature Also deepen continuously, the characters of ground object that many is hidden in the range of narrow spectrum is gradually found, has greatly accelerated remote sensing technology Development, high-spectrum remote-sensing is turned into one of most important research direction of 21 century remote sensing fields.
Different from multispectral remote sensing, spectral remote sensing technology using imaging spectrometer with nano level spectral resolution, with several Ten or hundreds of wave bands earth's surface thing is imaged simultaneously, be obtained in that the continuous spectrum information of atural object, the spy with " collection of illustrative plates " Property, all played an important role at aspects such as national economy, national defense construction, have been widely used for landmarks categorizations, target and visit The fields such as survey, agricultural monitoring, mineral map plotting, environmental management and national defense construction.
The classification of high spectrum image is an important content of target in hyperspectral remotely sensed image treatment and application, and its final goal is Unique classification logotype is assigned to each pixel in image.For the research and analysis of hyperspectral image data classification task, Main is exactly that the characters of ground object in image is told by high-spectral data, i.e., believed by analyzing original spectrum or other features Breath, different earth surface areas is marked off to come, such as meadow, farmland, waters, cities and towns, bridge, contributes to people to recognize and divide Analysis earth's surface situation.
High in face of hyperspectral image data dimension, data dependence is strong, and redundancy is high, the features such as local space uniformity, Conventional sorting technique is mainly started with from the following aspects：(1) rarefaction representation and dictionary learning, select a small amount of marked sample As dictionary, gone to represent other samples with the linear combination of dictionary sample；(2) supporting vector machine SVM classifier and its kernel function Correlating transforms, build new kernel function, such as polynomial kernel, adapt to the nonlinear Distribution of high-spectral data；(3) it is semi-supervised with Active Learning, the classifying quality under lifting small sample；(4) proposition of new grader and the combination of multi-categorizer；(5) feature is carried Take with combined transformation etc..Wherein conventional feature includes：1) itself spectral information characteristics of spectral signature, i.e. high spectrum image and its Related derived character；2) space characteristics, including high spectrum image texture, shape, morphological feature；3) sky-spectrum signature, i.e., it is empty Between feature and spectral signature be combined the feature for obtaining.
These features are started with from the inherent characteristic of high spectrum image, and the low layer spy that conversion is obtained is extracted by simple Levy.With the burning hot rise of deep learning in recent years, how using deep learning model framework, fully extracting has more preferably expression Property high spectrum image high-level characteristic, lifted classification hyperspectral imagery precision, increasingly become domestic and foreign scholars fall over each other research Focus.
Deep learning is a kind of Feature Extraction Technology, and it is developed from nerual network technique, layering by way of pair Low-level feature carries out high abstraction, so as to obtain the more preferable method for expressing of feature.Conventional deep learning framework mainly has stack Self-encoding encoder SAE, depth confidence network DBN, convolutional neural networks CNN, recurrent neural network RNN, they are widely used in The fields such as natural language processing, computer vision, speech recognition, bioinformatics, and achieve extraordinary effect.
Scholar is had at present by stack self-encoding encoder SAE, depth confidence network DBN and convolutional neural networks CNN depth Practise model and be incorporated into classification hyperspectral imagery.《Deep Learning-Based Classification of Hyperspectral Data》In, high-spectral data is carried out PCA dimensionality reductions by Yushi Chen, chooses the pixel in rectangular window Connect into and think characteristic vector as local spatial feature, then coupled together with original spectrum feature, be input into as low-level feature Build stack self-encoding encoder SAE models.《Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network》In, it is special that Yushi Chen build low layer using method same as above Levy, and be input into the depth confidence network DBN model for building.The classifying quality of this method is general, and accuracy is not high, and In the presence of many deficiencies, such as directly using spectral signature as input feature vector, comprising random noise too much, identification is weaker, nothing Method obtains good classifying quality；And in local spatial feature extraction, simply pick all pixels of neighborhood and be not added with Treatment, the then pixel for wherein being differed greatly with center pixel can badly influence nicety of grading.
The content of the invention
It is an object of the invention to be directed to above-mentioned the deficiencies in the prior art, a kind of bloom based on recurrent neural network is proposed Spectrum image classification method, to build the degree of purity more more preferable low-level feature of high-class effect, while strengthening to picture in local space The exploration of correlation between element, improves the effect of important pixel, reduces the influence of useless pixel, and low-level feature is abstract to sentence Other property high-level semantics features higher, so as to more fully using the characteristic of high spectrum image, improve nicety of grading.
To achieve the above object, technical scheme includes as follows：
(1) be input into a panel height spectrum picture, the high spectrum image include K pixel, B EO-1 hyperion spectral coverage, c class atural objects, Wherein K=K1×K2, K1Represent the length of high spectrum image, K2The width of high spectrum image is represented, each pixel of image is one Sample, each sample represents that the intrinsic dimensionality of sample is B with a characteristic vector, and 10% sample is selected in every class atural object This composition training sample set, is left 90% sample composition test sample collection；
(2) the principal component gray-scale map of high spectrum image is filtered using Gabor filter, obtains high spectrum image Spatial texture featureWherein R represents real number field, and g is spatial texture feature vector dimension；
(3) rarefaction representation coefficient of each pixel in high spectrum image is calculated using the method for rarefaction representation, bloom is obtained The rarefaction representation feature of spectrogram pictureWherein m is the dimension of rarefaction representation characteristic vector；
(4) by the spatial texture feature F of high spectrum image1With rarefaction representation feature F2Stacked combination is into high spectrum image Low-level featureL is the dimension of low-level feature vector, l=g+m；
(5) in high spectrum image low-level feature matrix F, window is built centered on each sample, extracts the office of sample Portion's space characteristics block, and the local space sequence signature of sample is built using the similitude between sample；
(6) recurrent neural networks model is built by time step long number of number of samples in window, and is input into training sample Local space sequence signature and corresponding class label repetitive exercise recurrent neural networks model parameter, the recurrence for being trained Neural network model；
(7) the local space sequence signature of test sample is input in the recurrent neural networks model for training, is obtained Classification category, completes classification.
The invention has the advantages that：
(1) present invention gets up the spatial texture feature and rarefaction representation feature integration of high spectrum image, and this low layer is special Levy both comprising high spectrum image pixel samples local spatial information, and comprising rarefaction representation of the pixel samples on other samples Information, such low-level feature degree of purity and identification are higher, more preferable for classification task effect；
(2) present invention extracts the local space sequence of high spectrum image on the basis of high spectrum image local spatial feature Row feature, not only obtains local spatial information, also explores the similarity information between each pixel samples in local space, carries The effect of important pixel high, reduces the influence of useless pixel, improves classifying quality；
(3) present invention utilizes the recurrent neural networks model for being usually used in natural language processing field, by recurrent neural net The temporal characteristicses of network combine with high spectrum image local space sequence information, can effectively integrate high spectrum image local empty Between context relation, be extracted as high-level semantics features by low-level feature is abstract, take full advantage of the characteristic of EO-1 hyperion, improve Classification accuracy rate.
Brief description of the drawings
Fig. 1 is schematic flow sheet of the invention；
Fig. 2 is recurrent neural networks model schematic diagram；
Fig. 3 is the Indian Pines images that present invention emulation is used；
Fig. 4 is the classification results comparison diagram of the present invention and existing method to Indian Pines images.
Specific embodiment
Reference picture 1, specific implementation step of the invention includes：
Step 1, is input into high spectrum image.
It is input into a width three-dimensional matrice high spectrum image, the high spectrum image includes K pixel samples, B EO-1 hyperion spectral coverage, C class atural objects, wherein K=K1×K2, K1Represent the length of high spectrum image, K2The width of high spectrum image is represented, is selected in every class atural object 10% sample is selected as training sample, is left 90% sample as test sample.
Step 2, obtains the spatial texture feature F of high spectrum image1。
2a) enter line translation to high spectrum image using PCA, k=10 principal component gray-scale map before extracting；
43 kinds of direction Gabor filters of yardstick 2b) are set, that is, 4 different Gabor kernel functions directions and 3 are set Individual different sinusoidal plane wave wavelength, obtains 12 Gabor filters, and the kernel function of each Gabor filter is as follows：
Wherein, x'=xcos θ+ysin θ, y'=-xcos θ+ysin θ, x and y denotation coordination positional informations, λ represents sinusoidal The wavelength of plane wave, θ represents the direction of Gabor kernel functions,Phase deviation is represented, σ represents the standard deviation of Gaussian envelope, γ tables Show space aspect ratio；
2c) using 12 Gabor filters for setting respectively to each principal component ash in preceding k principal component gray-scale map Degree figure carries out Gabor filtering, obtains 12 filtered images of each principal component gray-scale map；
2d) by 12 × k filtered image stack together, the spatial texture feature of high spectrum image is obtainedWherein R represents real number field, and g=12 × k is spatial texture characteristic vector length.
Step 3, obtains the rarefaction representation feature F of high spectrum image2。
3a) 1% sample is randomly selected from the c class training samples of high spectrum image per class build son as dictionary atom Dictionary, wherein the sub- dictionary of the i-th class isI=1,2 ..., c,Represent the i-th class J-th dictionary atom, j=1,2 ..., mi, miRepresent the dictionary atom number of the i-th class；
3b) c sub- dictionary is lined up, an entirety is merged into, total structuring dictionary D=[D are obtained1 ... Di ... Dc], D ∈ RB×mIt is a two-dimensional matrix, represents total structuring dictionary that the sub- dictionary of all classes is constituted, m=m1+…+ mi+…+mcRepresent the sub- dictionary atom number sum of all categories；
The rarefaction representation vector of each pixel 3c) is solved using orthogonal matching pursuit algorithm, i.e., by orthogonal matching pursuit Algorithm optimization equation below, obtains rarefaction representation vector αs of each pixel x on structuring dictionary D in high spectrum image：
S.t.D α=x
Wherein | | α | |0Expression takes 0 norm to α；
3d) by the rarefaction representation vector α of all samples in high spectrum image, constituted according to raw image data correspondence position One rarefaction representation eigenmatrix of three-dimensional
Step 4, by spatial texture featureWith rarefaction representation featureStacked combination is got up, and is obtained To the low-level feature matrix of high spectrum imageWherein l=g+m is low-level feature vector length.
Step 5, builds the local space sequence signature of sample.
5a) in high spectrum image low-level feature matrix F, centered on each sample x, size is built by the length of side of w=9 It is the window of w × w, extracts the local spatial feature block of x, then the local spatial feature block of x includes w2=81 samples, wherein often Individual sample is a low-level feature vector of a length of l；
Each sample is big with the similarity of central sample x in 5b) calculating local spatial feature block according to Euclidean distance formula Small, the smaller similarity of Euclidean distance is bigger, and each sample is lined up from big to small according to similarity, obtains sample x's Local space sequence signature
Step 6, using training sample set and its correspondence class label training recurrent neural networks model.
It is 6a) time step long number T with number of samples in window, builds input layer and hidden node number is the recurrence of l Neural network model, wherein T=w × w=81；
The local space sequence signature and corresponding class label of training sample 6b) are input to recurrent neural network mould Type, each low-level feature vector that will be in sample local space sequence signature is input into each corresponding time step, tool Body method is by t-th low-level feature x in the local space sequence signature of any one training sample xtInput recurrent neural net T-th time step of network, by the input x of the time steptWith the hidden layer state s of step-length at previous i.e. the t-1t-1Altogether With the hidden layer state s for constituting the time stept：
st=σ (Uxt+Wst-1),
Wherein U represents input layer to the weight matrix of hidden layer, and W represents hidden layer to the weight matrix of hidden layer, and σ represents non-linear Activation primitive, σ of the invention selects ReLU functions, then last output o81By the hidden layer state s of last time step81 Determine, i.e.,：
Wherein V represents hidden layer to the weight matrix of output layer,Nonlinear activation function is represented, this exampleSelection Softmax functions, then using the parameter in the back-propagation method repetitive exercise recurrent neural networks model by the time, repeatedly Generation 200 times after stop, the recurrent neural networks model for being trained.
Step 7, the local space sequence signature of test sample is input in the recurrent neural networks model for training, and is obtained To classification category, classification is completed.
Effect of the invention can be further illustrated by following emulation experiment：
1. simulated conditions:
Emulation experiment uses Indian Pines images, the image to be pushed away by NASA's NASA jets in June, 1992 Unloaded visible ray/Infrared Imaging Spectrometer the AVIRIS for entering laboratory is obtained in the Indiana northwestward, as shown in figure 3, image Size is 145 × 145, totally 220 wave bands, and the wave band that removal noise and air and waters absorb also has 200 wave bands, totally 16 Class terrestrial object information, as shown in table 1.
Emulation experiment is Intel Core i5-4210, dominant frequency 2.90GHz in CPU, and the Windows 10 for inside saving as 8G is Carried out with Python on system.
16 class data in table 1Indian Pines images
Classification Item name Number of samples Classification Item name Number of samples
1 Alfalfa 46 9 Oats 20
2 Corn-notill 1428 10 Soybean-notill 972
3 Corn-mintill 830 11 Soybean-mintill 2455
4 Corn 237 12 Soybean-clean 593
5 Grass-pasture 483 13 Wheat 205
6 Grass-trees 730 14 Woods 1265
7 Grass-pasture-mowed 28 15 Buildings-Grass-Trees-Drives 386
8 Hay-windrowed 478 16 Stone-Steal-Towers 93
2. simulation parameter：
10% as training sample, remaining 90% used as test sample for the unification selection of above emulation experiment, SVM punishment because Son is set to 491；In SRC methods, dictionary directly is constituted with training sample, degree of rarefication is set to 10；In SOMP methods, directly use Training sample constitutes dictionary, and window size w is set to 9, and degree of rarefication is set to 30；In the present invention, PCA conversion retains preceding 10 masters Composition, frequency selection { 0.25,0.5,0.75 } 3 kinds of yardsticks of Gabor filter, set direction4 kinds of directions Totally 12 wave filters, rarefaction representation builds dictionary per class sample random selection 1%, and degree of rarefication is set to 30, and window size w is set It is 9, timing node number T is set to 81.
3. emulation content and result：
High spectrum image Indian Pine are classified with existing three kinds of common methods using the present invention, it is conventional Three kinds of methods are respectively：Based on supporting vector machine svm classifier method, based on the sorting technique of rarefaction representation SRC, based on partial zones The rarefaction representation SOMP sorting techniques in domain.
Indian Pines images are classified with above-mentioned three kinds of common methods with the present invention, as a result as shown in figure 4, its Middle Fig. 4 a are the classification results figures for using SVM methods, and Fig. 4 b are the classification results figures for using rarefaction representation SRC methods, and Fig. 4 c are to use The classification results figure of SOMP methods, Fig. 4 d are classification results figures of the invention.Figure 4, it is seen that compared to conventional three The method of kind, result figure of the invention becomes apparent from totally, and local space uniformity and edge uniformity are all better than existing method, Nicety of grading is higher.
The present invention respectively carries out 10 emulation experiments with other various methods, and the average value for taking classification results divides as final Class accuracy, including overall accuracy (OA), per class average accuracy (AA) and Kappa coefficient (Kappa), as a result such as the institute of table 2 Show.
The classification accuracy rate of the present invention of table 2 and other method
Method OA (%) AA (%) Kappa
SVM 81.24 74.06 0.79
SRC 68.53 64.23 0.64
SOMP 95.27 83.48 0.95
The present invention 97.17 95.84 0.97
As seen from Table 2, SOMP methods of the invention and existing are due to containing local spatial information, compared to only with single picture The SVM methods and SRC methods of prime information, classification accuracy rate is substantially higher, and the present invention had both incorporated spatial texture feature With the information of rarefaction representation feature, and the information between local space pixel is fully excavated, and will with recurrent neural networks model It is the identification more preferable high-level semantics features of representative higher that low-level feature is extracted, and can obtain classification accuracy rate higher, All well other three kinds of methods on overall accuracy, average accuracy, Kappa coefficient.
To sum up, the spatial texture feature and rarefaction representation feature integration of high spectrum image are low-level feature by the present invention, and Local space sequence signature is extracted on the basis of local spatial feature, and using the recurrent neural net in deep learning framework Network model is classified to high spectrum image, has both improve the degree of purity and identification of low-level feature, and high-spectrum is explored again Similarity information in the local space of picture between each pixel samples, improves the effect of important pixel, reduces useless pixel Influence, while the temporal characteristicses of recurrent neural network are combined with high spectrum image local space sequence information, can be with The context relation of high spectrum image local space is effectively integrated, high-level semantics features is extracted as by low-level feature is abstract, fully The characteristic of EO-1 hyperion is make use of, discrimination higher is obtained, compared with the existing methods with obvious advantage.

Claims (5)

1. a kind of hyperspectral image classification method based on recurrent neural network, including：
(1) a panel height spectrum picture is input into, the high spectrum image includes K pixel, B EO-1 hyperion spectral coverage, c class atural objects, wherein K =K1×K2, K1Represent the length of high spectrum image, K2The width of high spectrum image is represented, each pixel of image is a sample, Each sample represents that the intrinsic dimensionality of sample is B with a characteristic vector, and 10% sample composition is selected in every class atural object Training sample set, is left 90% sample composition test sample collection；
(2) the principal component gray-scale map of high spectrum image is filtered using Gabor filter, obtains the space of high spectrum image Textural characteristicsWherein R represents real number field, and g is spatial texture feature vector dimension；
(3) rarefaction representation coefficient of each pixel in high spectrum image is calculated using the method for rarefaction representation, high-spectrum is obtained The rarefaction representation feature of pictureWherein m is the dimension of rarefaction representation characteristic vector；
(4) by the spatial texture feature F of high spectrum image1With rarefaction representation feature F2Low layer of the stacked combination into high spectrum image FeatureL is the dimension of low-level feature vector, l=g+m；
(5) in high spectrum image low-level feature matrix F, window is built centered on each sample, extracts the local empty of sample Between characteristic block, and the local space sequence signature of sample is built using the similitude between sample；
(6) recurrent neural networks model is built by time step long number of number of samples in window, and is input into the part of training sample Spatial sequence feature and corresponding class label repetitive exercise recurrent neural networks model parameter, the recurrent neural for being trained Network model；
(7) the local space sequence signature of test sample is input in the recurrent neural networks model for training, is classified Category, completes classification.
2. method according to claim 1, using Gabor filter to the principal component of high spectrum image wherein in step 2 Gray-scale map is filtered, and carries out as follows：
2a) enter line translation to high spectrum image using PCA, k=10 principal component gray-scale map before extracting；
4 different Gabor kernel functions directions sinusoidal plane wave wavelength different with 3 2b) is set, 12 Gabor filters are obtained Ripple device, the kernel function of each Gabor filter is as follows：
Wherein, x'=xcos θ+ysin θ, y'=-xcos θ+ysin θ, x and y denotation coordination positional informations, λ represents sinusoidal plane The wavelength of ripple, θ represents the direction of Gabor kernel functions,Phase deviation is represented, σ represents the standard deviation of Gaussian envelope, and γ represents empty Between aspect ratio；
2c) each the principal component gray-scale map in k principal component gray-scale map is entered respectively using 12 Gabor filters for setting Row Gabor is filtered, and obtains 12 filtered images of each principal component gray-scale map；
2d) by 12 × k filtered image stack together, the spatial texture feature of high spectrum image is obtainedG=12 × k representation space texture feature vector length.
3. method according to claim 1, calculates every in high spectrum image wherein in step 3 using the method for rarefaction representation The rarefaction representation coefficient of individual pixel, is carried out as follows：
3a) 1% sample is randomly selected from the c class training samples of high spectrum image per class build sub- word as dictionary atom Allusion quotation, wherein the sub- dictionary of the i-th class isI=1,2 ..., c,Represent the i-th class J-th dictionary atom, j=1,2 ..., mi, miRepresent the dictionary atom number of the i-th class；
3b) c sub- dictionary is lined up, an entirety is merged into, total structuring dictionary D=[D are obtained1 ... Di ... Dc], D ∈ RB×mIt is a two-dimensional matrix, represents total structuring dictionary that the sub- dictionary of all classes is constituted, m=m1+…+mi+… +mcRepresent the sub- dictionary atom number sum of all categories；
The rarefaction representation vector of each pixel 3c) is solved using orthogonal matching pursuit algorithm, i.e., by orthogonal matching pursuit algorithm Optimization equation below, obtains rarefaction representation vector αs of each pixel x on structuring dictionary D in high spectrum image：
argmin α | | α | | 0
S.t. D α=x
Wherein | | α | |0Expression takes 0 norm to α；
3d) by the rarefaction representation vector α of all samples in high spectrum image, one is constituted according to raw image data correspondence position Three-dimensional rarefaction representation eigenmatrix
4. method according to claim 1, builds the local space sequence signature of sample wherein in step 5, by following step Suddenly carry out：
5a) in high spectrum image low-level feature matrix F, centered on any one sample x, built by the window length of side of w=9 Size is w × w rectangular windows, extract the three-dimensional matrice that the local spatial feature block of x, i.e. size are w × w × l, then x Local spatial feature block includes w2=81 pixel samples, wherein each sample are a low-level feature vector of a length of l, l=g + m, g representation space texture feature vector length, m represent rarefaction representation characteristic vector length；
5b) calculate the similarity size of each pixel samples and center pixel sample x in local spatial feature block, and by each picture Element is lined up from big to small according to similarity, obtains the local space sequence signature of pixel samples x
5. method according to claim 1, builds recurrent neural networks model and training pattern parameter wherein in step 6, Carry out as follows：
It is that T, input layer and hidden node number are the recurrent neural networks model of l 6a) to build time step, wherein T=w × W=81, w represent the window length of side, and l=g+m, g representation space texture feature vector length, m represent that rarefaction representation characteristic vector is long Degree；
The local space sequence signature of training sample 6b) is input to recurrent neural networks model, will sample local space sequence Each low-level feature vector in row feature is input into each corresponding time step, and using the backpropagation by the time Parameter in method repetitive exercise recurrent neural networks model, the recurrent neural networks model for being trained.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103514456A (en) * 2013-06-30 2014-01-15 安科智慧城市技术（中国）有限公司 Image classification method and device based on compressed sensing multi-core learning
CN104036289A (en) * 2014-06-05 2014-09-10 哈尔滨工程大学 Hyperspectral image classification method based on spatial and spectral features and sparse representation
CN104091151A (en) * 2014-06-30 2014-10-08 南京信息工程大学 Vehicle identification method based on Gabor feature extraction and sparse representation
CN104298999A (en) * 2014-09-30 2015-01-21 西安电子科技大学 Hyperspectral feature leaning method based on recursion automatic coding
US9152881B2 (en) * 2012-09-13 2015-10-06 Los Alamos National Security, Llc Image fusion using sparse overcomplete feature dictionaries
WO2016075081A1 (en) * 2014-11-10 2016-05-19 Lego A/S System and method for toy recognition

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9152881B2 (en) * 2012-09-13 2015-10-06 Los Alamos National Security, Llc Image fusion using sparse overcomplete feature dictionaries
CN103514456A (en) * 2013-06-30 2014-01-15 安科智慧城市技术（中国）有限公司 Image classification method and device based on compressed sensing multi-core learning
CN104036289A (en) * 2014-06-05 2014-09-10 哈尔滨工程大学 Hyperspectral image classification method based on spatial and spectral features and sparse representation
CN104091151A (en) * 2014-06-30 2014-10-08 南京信息工程大学 Vehicle identification method based on Gabor feature extraction and sparse representation
CN104298999A (en) * 2014-09-30 2015-01-21 西安电子科技大学 Hyperspectral feature leaning method based on recursion automatic coding
WO2016075081A1 (en) * 2014-11-10 2016-05-19 Lego A/S System and method for toy recognition

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MENGQI JI ETAL.: "Preprocessing-free surface material classification using convolutional neural networks pretrained by sparse Autoencoder", 《2015 IEEE 25TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)》 *
朱可: "基于稀疏表示的人脸表情识别", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (50)

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