Essential processing methods of hyperspectral images of agricultural and food products

Hyperspectral images integrate spatial and spectral details together. They can provide valuable information about both external physical and internal chemical characteristics of agricultural and food products rapidly and non-destructively. Despite rapid improvements in instruments and acquisition techniques, the collected high-quality hyperspectral images still contain much useless information, like uneven illumination, background, specular reflection, and bad pixels that need to be removed. That is, hyperspectral image preprocessing is necessary for almost each hyperspectral image to get pure images or pixels, or to reduce negative influences on the subsequent detection, classification, and prediction analysis. This manuscript will enumerate some possible solutions to deal with issues mentioned above before further image analyzing. The advantages and disadvantages of different methods when dealing with a specific problem are also discussed. Obtained clean images or pure signals can be used for further data analysis. Finally, post-processing of hyperspectral images can be carried out to enhance the classification result of images or to generate chemical images/distribution maps to show spatial component concentration distributions of non-homogeneous samples.