We apply time-domain spectroscopy (THz) imaging technology to perform nondestructive detection on three industrial ceramic matrix composite (CMC) samples and one silicon slice with defects. In terms of spectrum recognition, a low-resolution THz spectrum image results in an ineffective recognition on sample defect features. Therefore, in this article, we propose a spectrum clustering recognition model based on t-distribution stochastic neighborhood embedding (t-SNE) to address this ineffective sample defect recognition. Firstly, we propose a model to recognize a reduced dimensional clustering of different spectrums drawn from the imaging spectrum data sets, in order to judge whether a sample includes a feature indicating a defect or not in a low-dimensional space. Second, we improve computation efficiency by mapping spectrum data samples from high-dimensional space to low-dimensional space by the use of a manifold learning algorithm (t-SNE). Finally, to achieve a visible observation of sample features in low-dimensional space, we use a conditional probability distribution to measure the distance invariant similarity. Comparative experiments indicate that our model can judge the existence of sample defect features or not through spectrum clustering, as a predetection process for image analysis.

中文翻译：

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基于t-SNE的新型太赫兹差分谱聚类识别方法

我们应用时域光谱（THz）成像技术对三个工业陶瓷基复合材料（CMC）样品和一个有缺陷的硅片进行无损检测。在频谱识别方面，低分辨率太赫兹频谱图像导致无法有效识别样品缺陷特征。因此，在本文中，我们提出了一种基于t分布随机邻域嵌入（t-SNE）的频谱聚类识别模型，以解决这种无效的样本缺陷识别问题。首先，我们提出一个模型来识别从成像光谱数据集得出的不同光谱的降维聚类，以便判断样本是否在低维空间中包含指示缺陷的特征。第二，我们通过使用流形学习算法（t-SNE）将频谱数据样本从高维空间映射到低维空间，从而提高了计算效率。最后，为了在低维空间中实现对样本特征的可见观察，我们使用条件概率分布来测量距离不变相似度。比较实验表明，我们的模型可以通过光谱聚类判断样品缺陷特征的存在与否，作为图像分析的预检测过程。