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Proceedings Paper

Optimisation and evaluation of hyperspectral imaging system using machine learning algorithm
Author(s): Gajendra Suthar; Jung Y. Huang; Santhosh Chidangil
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Paper Abstract

Hyperspectral imaging (HSI), also called imaging spectrometer, originated from remote sensing. Hyperspectral imaging is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the objects physiology, morphology, and composition. The present work involves testing and evaluating the performance of the hyperspectral imaging system. The methodology involved manually taking reflectance of the object in many images or scan of the object. The object used for the evaluation of the system was cabbage and tomato. The data is further converted to the required format and the analysis is done using machine learning algorithm. The machine learning algorithms applied were able to distinguish between the object present in the hypercube obtain by the scan. It was concluded from the results that system was working as expected. This was observed by the different spectra obtained by using the machine-learning algorithm.

Paper Details

Date Published: 6 October 2017
PDF: 7 pages
Proc. SPIE 10438, Emerging Imaging and Sensing Technologies for Security and Defence II, 104380L (6 October 2017); doi: 10.1117/12.2296863
Show Author Affiliations
Gajendra Suthar, Manipal Univ. (India)
Jung Y. Huang, National Chiao Tung Univ. (Taiwan)
Santhosh Chidangil, Manipal Univ. (India)

Published in SPIE Proceedings Vol. 10438:
Emerging Imaging and Sensing Technologies for Security and Defence II
Keith L. Lewis; Richard C. Hollins; Gerald S. Buller; Robert A. Lamb, Editor(s)

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