CN111401426A - Small sample hyperspectral image classification method based on pseudo label learning - Google Patents

Small sample hyperspectral image classification method based on pseudo label learning Download PDF

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CN111401426A
CN111401426A CN202010167527.9A CN202010167527A CN111401426A CN 111401426 A CN111401426 A CN 111401426A CN 202010167527 A CN202010167527 A CN 202010167527A CN 111401426 A CN111401426 A CN 111401426A
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魏巍
李宇
张磊
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Abstract

The invention provides a small sample hyperspectral image classification method based on pseudo tag learning. Firstly, sampling surrounding pixels by taking a hyperspectral pixel point as a center to generate a hyperspectral sample, constructing a small sample data set by using a small number of labeled samples, and distributing soft-pseudo labels for unlabeled samples by using the small sample data set to obtain an auxiliary data set; then, training a two-branch deep neural network formed by a shared feature extractor and two different classifiers by using the small sample data set and the auxiliary data set; and finally, predicting labels for the test data by using the trained network to realize classification processing. According to the method, the potential information of the unlabeled sample is effectively utilized, so that the accuracy of classifying the hyperspectral images of the small samples can be improved.

Description

Small sample hyperspectral image classification method based on pseudo label learning
Technical Field
The invention belongs to the technical field of hyperspectral image processing, and particularly relates to a small sample hyperspectral image classification method based on pseudo tag learning.
Background
Unlike conventional machine learning methods, deep neural networks have strong feature representation capability, and in order to solve the problem of gradient disappearance of deep neural networks, documents of' Zhong hong Z, L i J, L uo Z, et al. spectral-spatial similarity analysis, A3-D deep learning frame work [ J ]. eetransactions on geon similarity analysis and prediction, 2017,56(2): 7, 8456, 2) use a space information model to extract a large amount of spectral data, and the neural network classification model is difficult to be used for efficient depth training.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a small sample hyperspectral image classification method based on pseudo tag learning. Firstly, sampling surrounding pixels by taking a hyperspectral pixel point as a center to generate a hyperspectral sample, constructing a small sample data set by using a small number of labeled hyperspectral samples, and distributing soft-pseudo labels for unlabeled samples by using the small sample data set to obtain an auxiliary data set; then, training a two-branch deep neural network formed by a shared feature extractor and two different classifiers by using the small sample data set and the auxiliary data set; and finally, predicting labels for the test data by using the trained network to realize classification processing. According to the method, the potential information of the unlabeled sample is effectively utilized, so that the accuracy of classifying the hyperspectral images of the small samples can be improved.
A small sample hyperspectral image classification method based on pseudo tag learning comprises the following steps:
step 1: sampling surrounding pixels by taking hyperspectral pixel points as centers to generate hyperspectral data samples, and forming a small sample dataset T { (x) by using samples with labels1,y1),(x2,y2),…,(xn,yn) } of whichIn (1),
Figure BDA0002407984800000011
Figure BDA0002407984800000021
representing the ith marker sample, w is the size of a sampling window, w is 3, 5 or 7, d is the wave band number of the hyperspectral image,
Figure BDA0002407984800000022
for the label of the ith marking sample, label yiThe method adopts a form of unique hot coding, wherein the form is L-dimensional vector, L is category number, i is 1,2, …, n and n are the number of marked samples, and a test data set U is formed by unlabeled samples1,z2,…,zmAnd (c) the step of (c) in which,
Figure BDA0002407984800000023
representing the jth unlabeled sample, wherein j is 1,2, …, m is the number of the unlabeled samples, the number n of the labeled samples is L, 3, L or 5, L, and the number m of the unlabeled samples is the difference between the total number of the samples and the number of the labeled samples;
step 2: marking the sample h with the jth of the ith classijJth agent as class iij，i＝1,2,…,L,j＝1,2,…,in，inRepresenting the number of i-th type labeled samples; respectively calculating the p-th unlabeled sample zpDistance from all agents, p 1,2, …, m, and the agent in each class closest to it is selected as unlabeled sample zpReference Agents in this class, get unlabeled samples zpReference proxy sequence of
Figure BDA0002407984800000024
Wherein the content of the first and second substances,
Figure BDA0002407984800000025
represents an unlabeled sample zpA reference agent in class i; then according to
Figure BDA0002407984800000026
Figure BDA0002407984800000027
Calculating to obtain an unlabeled sample zpSoft-pseudo label vector of
Figure BDA0002407984800000028
To get the value of the ith element in (c), thereby obtaining the unlabeled sample zpSoft-pseudo label vector of
Figure BDA0002407984800000029
Wherein the content of the first and second substances,
Figure BDA00024079848000000210
representing reference agents
Figure BDA00024079848000000211
With unlabelled specimen zpThe distance between them, softmax (·) is a normalized exponential function; auxiliary data set is formed by all unmarked samples and soft-false labels thereof in test data set
Figure BDA00024079848000000212
And step 3: constructing a two-branch deep neural network, which comprises a feature extractor and two classifiers, wherein the two classifiers are a small sample classifier and an auxiliary sample classifier respectively, the feature extractor and the small sample classifier form a small sample classification branch of the network, and the feature extractor and the auxiliary sample classifier form an auxiliary sample classification branch of the network; the feature extractor is any convolutional neural network, and the classifier is only one or a plurality of fully-connected layers;
inputting a small sample data set into a small sample classification branch, inputting an auxiliary data set into an auxiliary sample classification branch, respectively extracting the auxiliary data set by a feature extractor to obtain distinguishing features of the auxiliary data set, and respectively obtaining category prediction labels of the auxiliary data set by different classifiers; setting that the loss functions of the small sample data set classifier and the auxiliary data set classifier are cross entropy functions, and performing optimization training on the network by adopting a random gradient descent method and an Adam optimizer to obtain a trained network;
and 4, step 4: and (4) inputting the test data set into the small sample classification branch in the trained network obtained in the step (3) to obtain a classification result.
The invention has the beneficial effects that: because the soft-pseudo label learning mode is adopted to distribute labels for the unmarked samples, the internal information of the unmarked samples can be effectively utilized; the auxiliary data set is used for increasing constraint for training the feature extractor of the small sample data set, overfitting of a classification model can be effectively prevented, and hyperspectral image classification precision is remarkably improved.
Detailed Description
The present invention is further illustrated by the following examples, which include, but are not limited to, the following examples.
The invention provides a small sample hyperspectral image classification method based on pseudo tag learning, which is basically realized in the following processes:
1. building a data set
Sampling surrounding pixels by taking a hyperspectral pixel point as a center to generate a hyperspectral cube (namely a sample), and forming a small sample dataset T by using a labeled sample, namely T { (x)1,y1),(x2,y2),…,(xn,yn) And (c) the step of (c) in which,
Figure BDA0002407984800000031
Figure BDA0002407984800000032
representing the ith labeled sample (namely, a labeled sample), w is the size of a sampling window (the value of 3, 5, 7 can be taken as equivalent), and d is the number of wave bands of the hyperspectral image;
Figure BDA0002407984800000033
label for ith sample, label yiThe method adopts a form of one-hot coding, namely L-dimensional vector, L is category number, i is 1,2, …, n, n is the number of marked samples, and a test data set U is formed by unlabeled samples, namely U is { z }1,z2,…,zmAnd (c) the step of (c) in which,
Figure BDA0002407984800000034
the value range of the number n of the marked samples is L, 3 x L and 5 x L, namely only 1, 3 and 5 samples with labels are provided in each class, the value range of the number m of the unmarked samples is the difference between the total number of the samples and the number of the marked samples, and m is provided on a plurality of reference hyperspectral data sets>10000。
2. Soft-pseudo label learning
The invention takes each marked sample as a proxy, and assigns a unique soft-pseudo label to each unmarked sample by calculating the distance between the unmarked sample and all the proxies.
First, a sample is marked
Figure BDA0002407984800000035
To be considered as a proxy:
agentij＝hij(1)
wherein, agentijJ-th agent, h, representing the i-th classijThe jth labeled sample of the ith class, i 1,2, …, L, j 1,2, …, in，inRepresenting the total number of samples of the ith class.
Respectively calculating the p-th unlabeled sample
Figure BDA0002407984800000036
The distance between the agent and all the agents is the jth agent of the ith classijFor example, the following steps are carried out:
Figure BDA0002407984800000037
wherein the content of the first and second substances,
Figure BDA0002407984800000038
representing agentijAnd sample zpThe distance between the two is sim (·,) similarity function, which can be selected arbitrarilyDistance formula.
Selecting a distance z in each classpNearest agent as unlabeled sample zpReference agents in this class to select zpReference proxy in class i
Figure BDA0002407984800000041
For example, the following steps are carried out:
Figure BDA0002407984800000042
unlabeled specimen zpReference proxy sequence of
Figure BDA0002407984800000043
That is, it can be obtained by equation 3.
Thus, the unlabeled sample zpSoft-pseudo label vector of
Figure BDA0002407984800000044
The value of the ith element in (b) can be obtained by:
Figure BDA0002407984800000045
where softmax (·) is a normalized exponential function, the input is mapped to real numbers between 0-1, and the normalized guaranteed sum is 1. Thereby obtaining an unlabeled sample zpSoft-pseudo label vector of
Figure BDA0002407984800000046
Figure BDA0002407984800000047
For each unlabeled sample zpAll (p ═ 1,2, …, m) are processed as above to obtain their corresponding soft-pseudo labels.
The auxiliary data set S is formed by all unmarked samples and soft-false labels thereof in the test data set, i.e.
Figure BDA0002407984800000048
Figure BDA0002407984800000049
3. Building a network and training
The invention uses a shared feature extractor to extract the discriminant features on the auxiliary data set and the small sample data set, and constructs different classifiers on the two data sets respectively to map the discriminant features to the label space. I.e. co-training the feature extractor F with the small sample data set T and the auxiliary data set SθTheta is a parameter of the feature extractor, different classifiers C (-) and C' (-) are trained on the T, S data set respectively to form a two-branch deep neural network, and hyperspectral image classification is achieved. The feature extractor can be any three-dimensional convolutional neural network structure, and the classifier can also be one or more layers of full-connection layer structures.
For the classification branch of a small sample dataset T, sample xkPredictive label of
Figure BDA00024079848000000410
Can be obtained by the following formula:
Figure BDA00024079848000000411
likewise, for the classification branch of the secondary data set S, the predicted values of the pseudo-tags
Figure BDA00024079848000000412
And sample zkThe relationship between (a) and (b) can be obtained by the following formula:
Figure BDA00024079848000000413
on the classification branch of the small sample dataset T, the parameters in the neural network are updated using the following cross-entropy function as the loss function:
Figure BDA0002407984800000051
wherein, LfewRepresenting the loss function on the classification branch of a small sample dataset, 1[j＝yk]Means j ═ ykThe value is 1 when the value is out, and the value is 0 when the value is out.
Likewise, the parameters of the classification branch of the secondary data set S may also be updated with the following equation:
Figure BDA0002407984800000052
wherein the content of the first and second substances,
Figure BDA0002407984800000053
presentation label
Figure BDA0002407984800000054
The (j) th element of (a),
Figure BDA0002407984800000055
presentation label
Figure BDA0002407984800000056
L th element ofauxRepresenting the loss function on the classification branch of the secondary data set.
And (3) optimizing the two cross entropy loss functions by using a random gradient descent method and an Adam optimizer to obtain a trained network.
4. Sorting process
Test data set U ═ z1,z2,…,zmAnd (4) inputting the training small sample data set obtained in the step (3) to classify one sample to obtain a classification result.
To verify the effectiveness of the method of the invention, experiments on three reference data sets, Indian pipes, PaviaUniversity and salanas were compared with the method proposed in the document "Zhong Z, L i J, L uo Z, et al spectral-spatial residual network for hyperspectral image classification: A3-D discarding frame [ J ]. IEEE Transactions on Geoscience and removal Sensing,2017,56(2): 847-.

Claims (1)

1. A small sample hyperspectral image classification method based on pseudo tag learning comprises the following steps:
step 1: sampling surrounding pixels by taking hyperspectral pixel points as centers to generate hyperspectral data samples, and forming a small sample dataset T { (x) by using samples with labels1,y1),(x2,y2),…,(xn,yn) And (c) the step of (c) in which,
Figure FDA0002407984790000011
Figure FDA0002407984790000012
representing the ith marker sample, w is the size of a sampling window, w is 3, 5 or 7, d is the wave band number of the hyperspectral image,
Figure FDA0002407984790000013
for the label of the ith marking sample, label yiThe method adopts a form of unique hot coding, wherein the form is L-dimensional vector, L is category number, i is 1,2, …, n and n are the number of marked samples, and a test data set U is formed by unlabeled samples1,z2,…,zmAnd (c) the step of (c) in which,
Figure FDA0002407984790000014
representing the jth unlabeled sample, wherein j is 1,2, …, m is the number of the unlabeled samples, the number n of the labeled samples is L, 3, L or 5, L, and the number m of the unlabeled samples is the difference between the total number of the samples and the number of the labeled samples;
step 2: marking the sample h with the jth of the ith classijJth agent as class iij，i＝1,2,…,L,j＝1,2,…,in，inRepresenting the number of i-th type labeled samples; respectively calculating the p-th unlabeled sample zpDistance from all agents, p 1,2, …, m, and the agent in each class closest to it is selected as unlabeled sample zpReference Agents in this class, get unlabeled samples zpReference proxy sequence of
Figure FDA0002407984790000015
Wherein the content of the first and second substances,
Figure FDA0002407984790000016
represents an unlabeled sample zpA reference agent in class i; then according to
Figure FDA0002407984790000017
Calculating to obtain an unlabeled sample zpSoft-pseudo label vector of
Figure FDA0002407984790000018
To get the value of the ith element in (c), thereby obtaining the unlabeled sample zpSoft-pseudo label vector of
Figure FDA0002407984790000019
Wherein the content of the first and second substances,
Figure FDA00024079847900000110
representing reference agents
Figure FDA00024079847900000111
With unlabelled specimen zpThe distance between them, softmax (·) is a normalized exponential function; auxiliary data set is formed by all unmarked samples and soft-false labels thereof in test data set
Figure FDA00024079847900000112
And step 3: constructing a two-branch deep neural network, which comprises a feature extractor and two classifiers, wherein the two classifiers are a small sample classifier and an auxiliary sample classifier respectively, the feature extractor and the small sample classifier form a small sample classification branch of the network, and the feature extractor and the auxiliary sample classifier form an auxiliary sample classification branch of the network; the feature extractor is any convolutional neural network, and the classifier is only one or a plurality of fully-connected layers;
inputting a small sample data set into a small sample classification branch, inputting an auxiliary data set into an auxiliary sample classification branch, respectively extracting the auxiliary data set by a feature extractor to obtain distinguishing features of the auxiliary data set, and respectively obtaining category prediction labels of the auxiliary data set by different classifiers; setting that the loss functions of the small sample data set classifier and the auxiliary data set classifier are cross entropy functions, and performing optimization training on the network by adopting a random gradient descent method and an Adam optimizer to obtain a trained network;
and 4, step 4: and (4) inputting the test data set into the small sample classification branch in the trained network obtained in the step (3) to obtain a classification result.
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