Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway.
Forensic Sci Int. 2020 Jun;311:110194. doi: 10.1016/j.forsciint.2020.110194. Epub 2020 Feb 12.
Ink analysis is an important tool in forensic science and document analysis. Hyperspectral imaging (HSI) captures large number of narrowband images across the electromagnetic spectrum. HSI is one of the non-invasive tools used in forensic document analysis, especially for ink analysis. The substantial information from multiple bands in HSI images empowers us to make non-destructive diagnosis and identification of forensic evidence in questioned documents. The presence of numerous band information in HSI data makes processing and storing becomes a computationally challenging task. Therefore, dimensionality reduction and visualization play a vital role in HSI data processing to achieve efficient processing and effortless understanding of the data. In this paper, an advanced approach known as t-Distributed Stochastic Neighbor embedding (t-SNE) algorithm is introduced into the ink analysis problem. t-SNE extracts the non-linear similarity features between spectra to scale them into a lower dimension. This capability of the t-SNE algorithm for ink spectral data is verified visually and quantitatively, the two-dimensional data generated by the t-SNE showed a better visualization and a greater improvement in clustering quality in comparison with Principal Component Analysis (PCA).
油墨分析是法医学和文件分析中的重要工具。高光谱成像（HSI）在整个电磁光谱范围内捕获大量窄带图像。HSI 是用于法医文件分析的非侵入性工具之一，特别是用于油墨分析。HSI 图像中多个波段的大量信息使我们能够对有问题的文件中的法医证据进行非破坏性诊断和识别。HSI 数据中存在大量波段信息，使得处理和存储成为一项具有挑战性的计算任务。因此，降维和可视化在 HSI 数据处理中起着至关重要的作用，可实现高效处理和轻松理解数据。在本文中，将一种称为 t-分布随机近邻嵌入（t-SNE）算法的高级方法引入到油墨分析问题中。t-SNE 提取光谱之间的非线性相似特征，将其缩放到较低维度。通过 t-SNE 算法对油墨光谱数据进行了可视化和定量验证，与主成分分析（PCA）相比，t-SNE 生成的二维数据具有更好的可视化效果和聚类质量的显著提高。
