Optimisation and evaluation of hyperspectral imaging system using machine learning algorithm

Gajendra Suthar, Jung Y. Huang, Santhosh Chidangil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationEmerging Imaging and Sensing Technologies for Security and Defence II
EditorsGerald S. Buller, Keith L. Lewis, Richard C. Hollins, Robert A. Lamb
PublisherSPIE
Volume10438
ISBN (Electronic)9781510613409
DOIs
Publication statusPublished - 01-01-2017
EventEmerging Imaging and Sensing Technologies for Security and Defence II 2017 - Warsaw, Poland
Duration: 13-09-201714-09-2017

Conference

ConferenceEmerging Imaging and Sensing Technologies for Security and Defence II 2017
CountryPoland
CityWarsaw
Period13-09-1714-09-17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Suthar, G., Huang, J. Y., & Chidangil, S. (2017). Optimisation and evaluation of hyperspectral imaging system using machine learning algorithm. In G. S. Buller, K. L. Lewis, R. C. Hollins, & R. A. Lamb (Eds.), Emerging Imaging and Sensing Technologies for Security and Defence II (Vol. 10438). [104380L] SPIE. https://doi.org/10.1117/12.2296863