Hyperspectral imaging is utilised in many applications, where measured data are processed, interpreted and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. In this thesis, various methods were proposed and applied to crop reflectance data (acquired by hand-held spectrometers) to detect, characterise and quantify disease severity and plant density. Furthermore, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. However, the large size of these images raises the need for efficient data reduction. A new type of self- organising weighted neural networks was proposed and used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images. Finally, the analysis can be reversed to generate high resolution spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous image is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.