Classification of materials through the integration of spectral and spatial features from hyperspectral data

Abstract

Images taken in grey levels and those based on the standard representation of colour
RGB extract diverse properties of different objects or materials in them, thus making their visual
separation possible through the use of image processing techniques. However, at times, these
materials bear certain similarities in their appearance, shape and/or colour that make their visual classification unfeasible. Counterpoint to this, hyperspectral images provide broad information about the luminous spectrum reflected in each of the elements of the image. This characterises their molecular properties in order to define more elaborate models that will provide greater precision in the classification. Despite these advantages, small variations in the chemical composition and/or the high variability between materials belonging to the same class, at times, make the obtaining of a robust classification through the use of spectral features in a simplistic manner impossible.

In order to provide a solution to this problem, this work sets out a methodology which allows, in the first place, the optimal reduction of high spectral dimensionality through the construction of spectral fuzzy sets which are bioinspired in the functioning of the cones of the human visual
system. These fuzzy sets minimise the redundant information that exists between adjacent bands of the spectrum, thus maximising its discriminating power in a similar manner to that which would be done by a "multispectral eye". Additionally, the spectral and spatial features of the elements of the image are integrated, which make possible the obtaining of a combined descriptor which in a more precise manner characterises the properties of the elements contained in that image. The theoretical model for the classification has been validated through the use of samples from materials for recycling from waste electrical and electronic equipment (WEEE). The obtained results show an increase in the classification rate from 44%, by only using colour information, to 56% via the use of spectral information through classic methods, up to 98%, through the extraction and integration of the proposed spectral-spatial features.

Date of Award	2009
Original language	English
Awarding Institution	Universidad del País Vasco (UPV/EHU)