A Spectral Feature Based CNN Long Short-Term Memory Approach for Classification
A Spectral Feature Based CNN Long Short-Term Memory Approach for Classification
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DOI：
10.1109/icicip47338.2019.9012180
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
J. Rochac;N. Zhang;Jiang Xiong
中科院分区：
文献类型：
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作者：
J. Rochac;N. Zhang;Jiang Xiong
本文提出了一种使用卷积神经网络（PCA 18 + GDA 100 +CNN LSTM）的高斯数据增强辅助深度学习，用于分析最先进的红外背向散射成像光谱（IBIS）图像。PCA和数据增强方法都用于预处理分类输入，并以可比的准确度进行预测。最初，PCA用于减少特征的数量。我们使用了18个主成分的基础上的累积方差，其中合计99.92%。GDA也被用来增加样品的数量。然后使用CNN-LSTM（长短期记忆）对IBIS高光谱图像进行多类分类。进行实验，并从K=20的K折交叉验证收集结果。用混淆矩阵进行分析，平均准确率为99%。
This paper presents a Gaussian data augmentation-assisted deep learning using a convolutional neural network (PCA18+GDA100+CNN LSTM) on the analysis of the state-of-the-art infrared backscatter imaging spectroscopy (IBIS) images. Both PCA and data augmentation methods were used to preprocess classification input and predict with a comparable degree of accuracy. Initially, PCA was used to reduce the number of features. We used 18 principal components based of the cumulative variance, which totaled 99.92%. GDA was also used to increase the number of samples. CNN-LSTM (long short-term memory) was then used to perform multiclass classification on the IBIS hyperspectral image. Experiments were conducted and results were collected from the K-fold cross-validation with K=20. They were analyzed with a confusion matrix and the average accuracy is 99%.