WO2020233207A1 - 一种基于半监督学习策略的高光谱数据分析方法 - Google Patents
一种基于半监督学习策略的高光谱数据分析方法 Download PDFInfo
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- RZVAJINKPMORJF-UHFFFAOYSA-N Acetaminophen Chemical compound CC(=O)NC1=CC=C(O)C=C1 RZVAJINKPMORJF-UHFFFAOYSA-N 0.000 description 4
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- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- RFSUNEUAIZKAJO-ARQDHWQXSA-N Fructose Chemical compound OC[C@H]1O[C@](O)(CO)[C@@H](O)[C@@H]1O RFSUNEUAIZKAJO-ARQDHWQXSA-N 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
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- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
Definitions
- the invention belongs to the technical field of hyperspectral nondestructive testing, and specifically relates to a hyperspectral data analysis method based on a semi-supervised learning strategy.
- Hyperspectral-based quantitative analysis technology has a wide range of application scenarios, including food adulteration detection, fruit sugar content detection, microbial content detection, and organic matter content detection.
- Commonly used hyperspectral quantitative analysis algorithms include partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multiple linear regression (MLR) and other methods, but the model accuracy and model robustness of the quantitative analysis algorithm are still Need to be further improved.
- PLSR partial least squares regression
- LS-SVM least squares support vector machine
- MLR multiple linear regression
- the convolutional neural network has been proven to have very strong ability to analyze complex information, and the convolutional network has been well applied in the classification of remote sensing hyperspectral data, its application in the quantitative analysis of hyperspectral data still has Greater difficulty.
- the main reason is that in practical applications, hyperspectral data samples, especially calibrated samples are difficult to obtain, and a small sample size brings a very large risk of overfitting.
- the label value in the quantitative analysis is a continuous analog quantity, so the network structure design cannot be equal to the semi-supervised classification problem, and it is impossible to use the k+1 class or k classification to fight the network to achieve regression;
- the classification problem Mainly use the generated data to help determine the boundary value of the classification, and in the regression problem, it is necessary to use the generated data to smooth the sample quantitative value distribution. Therefore, it is necessary to design a new network structure and loss function to realize the semi-supervised hyperspectral data quantitative analysis based on the generative countermeasure network, thereby improving the analysis accuracy.
- the present invention proposes a hyperspectral data analysis method based on a semi-supervised learning strategy.
- This method generates spectral samples by generating an adversarial network, and uses the generated samples to enhance the continuity of sample distribution and suppress over-fitting, thereby improving the accuracy of quantitative analysis of hyperspectral data.
- the present invention solves the above-mentioned problems through the following technical means:
- S3-1 Construct a generator network, which in turn consists of: fully connected layer-upsampling layer-convolutional layer-upsampling layer-convolutional layer-output layer, in which the number of nodes in the fully connected layer is 16*spectral band
- the convolution layer is a one-dimensional convolution, the size of the convolution kernel is 1 ⁇ 5, the number of convolution kernels ranges from 16 to 128, the up-sampling layer is 2 times up-sampling, the number of nodes in the output layer and the number of spectral bands Same, except for the output layer, the nonlinear excitation function is ReLU, and the output layer nonlinear excitation is the sigmoid function;
- S3-2 Construct a discriminator/regressor network, which in turn consists of: convolutional layer-pooling layer-convolutional layer-pooling layer-convolutional layer-pooling layer-output layer, of which the convolutional layer It is a one-dimensional convolution, the size of the convolution kernel is 1 ⁇ 5, the number of convolution kernels ranges from 16 to 128, the pooling layer is 1/2 down-sampling, and the output layer is two, one of which outputs the discriminator result , That is, the predicted value of the true and false of the spectral data, and the other output regressor result, that is, the predicted value of quantitative analysis. Except for the output layer, the nonlinear excitation function is leakyReLU, and the nonlinear excitation of the two output layers is the sigmoid function.
- the loss function of the discriminator/regressor is the sum of the loss function of the labeled sample and the unlabeled sample
- the unlabeled sample loss function is the sum of the loss function of the real unlabeled sample and the loss function of the generated sample:
- the label sample loss function is the regression loss function, that is, the mean square error between the quantitative analysis predicted value of the label sample and the quantitative label value:
- the unlabeled sample loss function is the cross entropy of the discriminator’s predicted value and the authenticity label:
- the generated sample loss function is the cross entropy of the discriminator’s predicted value and the authenticity label:
- the loss function of the generator is the sum of the loss function of the generated sample and the loss function of sample distribution matching:
- the loss function of the generated sample (L' unlabel_fake ) is the cross-entropy of the predicted value of the generated sample through the discriminator and the authenticity label of the generator.
- the authenticity label of the generated sample in the generator and the discriminator is opposite, so that the generator and the discriminator Formation of confrontation:
- the sample distribution matching loss function (L distribution ) is the mean square error of the distribution of the unlabeled sample predicted by the regressor and the quantitative analysis value of the generated sample:
- the sample distribution loss function exists to reduce the similarity between the existing sample and the quantitative analysis value distribution of the generated sample, so that the generated sample becomes a supplement to the existing unlabeled sample.
- Adopt the gradient descent method to alternately train the discriminator/regressor and generator until the root mean square error of the quantitative analysis predicted value and label value of the training set samples converges to less than a certain threshold or the number of training steps is greater than a certain threshold;
- the regressor in the trained generative confrontation network is used to obtain the quantitative analysis prediction value of the prediction set.
- the present invention has the following advantages:
- the present invention uses a generative adversarial network to generate samples, uses a sample distribution matching strategy to supplement the existing unlabeled sample set, and uses distributed matching generated samples to achieve a similar regularization effect to the regressor, thereby overcoming the quantitative analysis of deep learning
- the prone to overfitting problem improves the accuracy of hyperspectral quantitative analysis.
- FIG. 1 is a flowchart of the present invention.
- Figure 2(a) is a scatter plot of the actual and predicted values of the active ingredients of tablets obtained by using partial least squares regression (PLSR).
- PLSR partial least squares regression
- Figure 2(b) is a scatter plot of the real value and predicted value of the effective ingredient of the tablet obtained by directly using the convolutional network (CNN) without using the generative confrontation network.
- CNN convolutional network
- Fig. 2(c) is a scatter plot of the real value and predicted value of the effective ingredient of the tablet obtained by the method of the present invention.
- This example uses hyperspectroscopy to determine the active ingredients of acetaminophen tablets.
- a hyperspectral data analysis method based on a semi-supervised learning strategy the specific steps are as follows:
- S3-1 Construct a generator network, which in turn consists of: fully connected layer-upsampling layer-convolutional layer-upsampling layer-convolutional layer-output layer, in which the number of nodes in the fully connected layer is 64*45,
- the convolution layer is a one-dimensional convolution, the size of the convolution kernel is 1 ⁇ 5, the number of convolution kernels is 64, the upsampling layer is 2 times upsampling, the number of nodes in the output layer is 180, and the non-linear excitation function except the output layer ReLU, the nonlinear excitation of the output layer is a sigmoid function;
- S3-2 Construct a discriminator/regressor network, which in turn consists of: convolutional layer-pooling layer-convolutional layer-pooling layer-convolutional layer-pooling layer-output layer, of which the convolutional layer It is a one-dimensional convolution, the size of the convolution kernel is 1 ⁇ 5, the number of convolution kernels is 16, the pooling layer is 1/2 down-sampling, and the output layer is two, one of which outputs the result of the discriminator, that is, the true spectrum data Pseudo label, another output regressor result, that is, the effective component content value, except for the output layer, the nonlinear excitation function is leakyReLU, and the nonlinear excitation of the two output layers is the sigmoid function.
- the loss function of the discriminator/regressor is the sum of the loss function of the labeled sample and the unlabeled sample
- the unlabeled sample loss function is the sum of the loss function of the real unlabeled sample and the loss function of the generated sample:
- the unlabeled sample loss function is the cross entropy of the discriminator’s predicted value and the authenticity label:
- the generated sample loss function is the cross entropy of the discriminator’s predicted value and the authenticity label:
- the loss function of the generator is the sum of the loss function of the generated sample and the loss function of sample distribution matching:
- the loss function of the generated sample (L′ unlabel_fake ) is the cross entropy of the predicted value and the authenticity label of the sample generated by the generator through the discriminator.
- the authenticity label of the generated sample of the discriminator and the generator is opposite to form a confrontation:
- the sample distribution matching loss function (L distribution ) is the mean square error of the distribution of the unlabeled sample predicted by the regressor and the quantitative analysis value of the generated sample:
- Quantitative analysis value distribution of unlabeled samples predicted by the regression Quantitatively analyze the value distribution of the generated sample predicted by the regressor.
- the specific calculation method is: approximate the effective component value of the sample to a multinomial distribution of 100 items with a value of 0 to 1, and quantitatively analyze the value of the unlabeled sample of the current training batch predicted by the regressor and the generated sample with Approximate to the above 100 levels, and obtained through sample count statistics with
- the gradient descent method is used to alternately train the discriminator/regressor and the generator.
- the optimizer adopts the "Adam" optimizer, the learning rate is 0.0005, and the training step stops when the number of training steps reaches 1000;
- the training set and the prediction set were randomly sampled 10 times for calculation, and partial least square regression and convolutional network were used as a comparison method for calculation.
- the slice least squares regression method the number of principal components is determined by ten-fold cross-validation, and the parameters of the convolutional network are consistent with the regressor in the method of the present invention.
- the root mean square prediction error RMSEC of the training set obtained by the partial least square method is 1.01 ⁇ 0.46%, and the root mean square prediction error RMSEP of the prediction set is 3.79 ⁇ 1.06%; the training set obtained by the convolutional network
- the prediction error root mean square RMSEC is 1.45 ⁇ 0.71%, the prediction set root mean square error RMSEP is 5.84 ⁇ 1.77%;
- the training set obtained by the method of the present invention has a prediction error root mean square RMSEC of 2.42 ⁇ 0.49%, and the prediction set The root mean square RMSEP of prediction error is 2.56 ⁇ 0.88%.
- Figure 2 shows the scatter plot of the true value and the predicted value calculated by a random sampling.
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- 一种基于半监督学习策略的高光谱数据分析方法,其特征在于,包括如下步骤:S1.获取有标签的高光谱样本数据及无标签的高光谱样本数据;S2.构建样本训练集与预测集:将有标签的高光谱样本数据集D label作为训练集,无标签的高光谱样本数据集D unlabel作为预测集;S3.构建基于生成对抗网络的回归网络:S3-1.构建一个由全连接层、卷积层、上采样层、输出层组成的生成器网络;S3-2.构建一个由卷积层、池化层、输出层组成的判别器/回归器网络,该网络具有判别数据真伪与定量分析值两个输出;S4.构建生成对抗回归网络的损失函数:S4-1.判别器/回归器的损失函数为有标签样本和无标签样本损失函数之和,其中无标签样本损失函数为真实无标签样本损失函数(L unlabel_real)与生成样本损失函数(L unlabel_fake)之和:L D=L supervised+L unsupervisedL unsupervised=L unlabel_real+L unlabel_fakeS4-2.生成器的损失函数为生成样本的损失函数(L′ unlabel_fake)与样本分布匹配损失函数(L distribution)之和:L G=L′ unlabel_fake+L distribution其中样本分布匹配损失函数,是为了使生成样本和真实样本在样本空间中互为补集,从而达到抑制过拟合效果;S5.训练基于生成对抗的回归网络:采用梯度下降方法,交替训练判别器/回归器与生成器,直到训练集样本的定量分析预测值与标签值的均方根误差收敛至小于一个阈值或训练步数大于一个阈值;S6.采用训练好的生成对抗网络中的回归器得到预测集的定量分析预测值。
- 根据权利要求1所述的基于半监督学习策略的高光谱数据分析方法,其特征在于,步骤S2中构建样本训练集与预测集步骤包括:S2-1.将由标签样本作为训练集样本,无标签样本作为预测集样本,同时也用于半监督的训练;S2-2.在每个训练集和预测集的高光谱样本数据块中随机取m次n个有效像素光谱曲线的均值,作为样本增强,得到的有标签和无标签平均光谱数据集记为D label和D unlabel。
- 根据权利要求1所述的基于半监督学习策略的高光谱数据分析方法,其特征在于,步骤S3-1中所述生成器网络依次由:全连接层—上采样层—卷积层—上采样层—卷积层—输出层构成,其中全连接层节点数为16*光谱波段数,卷积层为一维卷积,卷积核尺寸为1×5,卷积核个数取值范围为16~128,上采样层为2倍上采样,输出层节点数与光谱波段数相同,除输出层外非线性激励函数为ReLU,输出层非线性激励为sigmoid函数。
- 根据权利要求1所述的基于半监督学习策略的高光谱数据分析方法,其特征在于,步骤S3-2中所述判别器/回归器网络依次由:卷积层—池化层—卷积层—池化层—卷积层—池化层—输出层构成,其 中卷积层为一维卷积,卷积核尺寸为1×5,卷积核个数取值范围为16~128,池化层为1/2下采样,输出层为2个,其中一个输出判别器结果,即光谱数据真伪预测值,另一个输出回归器结果,即定量分析预测值,除输出层外非线性激励函数为leakyReLU,两个输出层非线性激励为sigmoid函数。
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