Next Article in Journal
Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry
Previous Article in Journal
Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection
Previous Article in Special Issue
Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
Open AccessArticle

Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE

Department of Computer Science, Norwegian University of Science and Technology, 2802 Gjøvik, Norway
*
Author to whom correspondence should be addressed.
J. Imaging 2020, 6(5), 29; https://doi.org/10.3390/jimaging6050029
Received: 29 February 2020 / Revised: 28 April 2020 / Accepted: 30 April 2020 / Published: 5 May 2020
(This article belongs to the Special Issue Multispectral Imaging)
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. View Full-Text
Keywords: forensic document analysis; hyperspectral dimensionality reduction; forensic paper analysis; t-SNE; hyperspectral unsupervised clustering forensic document analysis; hyperspectral dimensionality reduction; forensic paper analysis; t-SNE; hyperspectral unsupervised clustering
Show Figures

Figure 1

MDPI and ACS Style

Melit Devassy, B.; George, S.; Nussbaum, P. Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE. J. Imaging 2020, 6, 29.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop