Automatically localising ROIs in hyperspectral images using background subtraction techniques

The use of snapshot hyperspectral cameras is becoming increasingly popular in agricultural scientific studies. One of the key steps in processing experimental hyperspectral data is to precisely locate the sample material under study and separate it from other background material, such as sampling instruments or equipment. This is very laborious work, especially for hyperspectral imaging scenarios where there might be a few hundred spectral images per sample. In this paper we propose a multiple-background modelling approach for automatically localising the Regions of Interest (ROIs) in hyperspectral images. The two key components of this method are i) modelling each spectral band individually and ii) applying a consensus algorithm to obtain the final ROIs for the whole hyperspectral image. Our proposed approach is able to achieve approximately a 14% improvement in ROIs detection in hyperspectral images compared to traditional video background modelling techniques.