Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

Ink Classification in Hyperspectral Images

Version 1 : Received: 4 July 2020 / Approved: 5 July 2020 / Online: 5 July 2020 (15:28:52 CEST)

How to cite: Bilal, M.; Ahmad, H.; Khizer Ali, M. Ink Classification in Hyperspectral Images. Preprints 2020, 2020070084 (doi: 10.20944/preprints202007.0084.v1). Bilal, M.; Ahmad, H.; Khizer Ali, M. Ink Classification in Hyperspectral Images. Preprints 2020, 2020070084 (doi: 10.20944/preprints202007.0084.v1).

Abstract

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

Subject Areas

Hyperspectral Imagery (HSI); Hyperspectral Document Imagery (HSDI); k-means clustering; Principal component analysis (PCA)

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