Investigation of low resolution point clouds for illumination correction in pushbroom hyperspectral images : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Machatronics at Massey University, Turitea Campus, Palmerston North, New Zealand

Figures 2.2 & 4.11 have been removed for copyright reasons, but may be accessed via the source listed in the Bibliography.Global food demand is predicted to double between 2015 and 2050. Current agricultural production is unable to facilitate this growth. Consequently, plant breeding must be accelerated to breed improved cultivars that can meet this demand. While technologies such as genomics are suitable for accelerating plant breeding, phenotyping lags behind and is currently considered the bottleneck. Consequently, imaging and remote sensing technologies are being used to provide quantitative, reliable phenotype information. One such technology; hyperspectral imaging can provide physiological, biophysical, and biochemical phenotypic information. While hyperspectral imaging has reached a substantial level of maturity in aerial and satellite based remote sensing applications, it is still underdeveloped in the close-range lab-based phenotyping scenario. In particular is the effect of illumination and complex plant geometry which affects the measured signal and is even more pronounced in the close range hyperspectral imaging. Methods for correction of illumination/geometry effects developed for aerial, and satellite-based imaging are unsuitable for close range hyperspectral imaging. Recently there has been an interest in fusing hyperspectral images with point clouds captured by 3D imaging devices to provide more comprehensive high dimensional phenotype information. However, one study focusses on the possibility of using 3D geometry of the plant to correct for the effects of illumination in hyperspectral images. This study investigates the use of low resolution point clouds captured with low cost devices for use in illumination modelling and correction of hyperspectral images acquired in close range lab-based scenario