Classification of materials in natural scenes using multi-spectral images

In this paper, a method suitable for distinguishing between different materials occurring in natural scenes using a multi-spectral camera is devised. Such a capability is useful in autonomous robot applications to help negotiating the environment as well as, e.g. applications intended to create large scale inventories of assets in the proximity of roads. The utilised sensor records a seven band multi-spectral image, of which six bands are in the visible range and one in the NIR (near-infrared) range. Many materials appearing similar if viewed by a common RGB camera, will show discriminating properties if viewed by a camera capturing a greater number of separated wavelengths. The approach in this paper is to combine the discriminating strength of the multi-spectral signature in each pixel and the corresponding nature of the surrounding texture. Local features, considering seven bands in each pixel and texture features such as GLCM and Fourier spectrum features are exploited to make the system more robust to different lighting conditions. Then classifiers built using SVM and AdaBoost are evaluated with very promising results, an average classification accuracy of 91.9% and 89.1%, respectively for a ten class problem.