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Title: Hyperspectral imaging : calibration and applications with natural scenes
Author: Ekpenyong, Nsikak Edet
ISNI:       0000 0004 2740 7708
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2013
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Hyperspectral imaging is a technique which combines spectral and spatial imaging methods. The technology is used in remote sensing, medicine, agriculture and forensics just to mention a few. Non-remote systems are developed by using sensor designs different from push-broom and whisk-broom methods, commonly found in remote sensing hyperspectral imaging systems. Images are commonly acquired by mounting various electronically tunable filters in front of monochromatic cameras and capturing a range of wavelengths to produce a spectral image cube. Illumination plays a major role during imaging, as both the camera and electronically tunable filter may suffer low transmission at the ends of the visible spectrum, resulting in a low signal to noise ratio. The work described in this thesis attempts to address two key objectives. The first was to identify the main sources of errors in a common design of focal-plane hyperspectral imaging system and devise ways of compensating for these errors. Calibration and characterization of a focal-plane hyperspectral imaging system included system noise characterization, stray-light compensation, flat field correction, image registration, input-output function characterization and calibration verification. The other was to apply imaging techniques to hyperspectral images. This included scene recognition using ratio indexing and spectral gradients. This comes from the underlying idea that due to the large number of bands contained in hyperspectral images, more information is available so better recognition results compared to RGB images. A novel approach for obtaining ratios for ratio indexing is proposed in this thesis. The imaging of archived materials from University of Manchester's John Rylands Library was also done. The aim was to produce high resolution hyperspectral images that will help in identifying accurate matches for colours used in document restoration at the Library.
Supervisor: Foster, David Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available