Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

• Published in: Journal of imaging Vol. 6; no. 5; p. 29
• Main Authors: Melit Devassy, Binu, George, Sony, Nussbaum, Peter
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 05.05.2020; MDPI
• Subjects: Algorithms; Cameras; Classification; Cluster analysis; Clustering; Cultural heritage; Data analysis; Datasets; forensic document analysis; forensic paper analysis; Forensic sciences; Forgery; Fourier transforms; hyperspectral dimensionality reduction; Hyperspectral imaging; hyperspectral unsupervised clustering; Methods; Narrowband; Nondestructive testing; Software; Spectrum analysis; t-SNE; Vector quantization
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging6050029

For a suspected forgery that involves the falsification of a document or its contents, the investigator will primarily analyze the document’s paper and ink in order to establish the authenticity of the subject under investigation. As a non-destructive and contactless technique, Hyperspectral Imaging (HSI) is gaining popularity in the field of forensic document analysis. HSI returns more information compared to conventional three channel imaging systems due to the vast number of narrowband images recorded across the electromagnetic spectrum. As a result, HSI can provide better classification results. In this publication, we present results of an approach known as the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm, which we have applied to HSI paper data analysis. Even though t-SNE has been widely accepted as a method for dimensionality reduction and visualization of high dimensional data, its usefulness has not yet been evaluated for the classification of paper data. In this research, we present a hyperspectral dataset of paper samples, and evaluate the clustering quality of the proposed method both visually and quantitatively. The t-SNE algorithm shows exceptional discrimination power when compared to traditional PCA with k-means clustering, in both visual and quantitative evaluations.