Neural Networks for the Texture Classification of Segmented Regions of Forward Looking Infrared Images

Texture can be interpreted as a measure of the 'edginess' about a pixel and can thus be described by edge co-occurrence matrices. The matrix can be decomposed using 2-dimensional orthogonal Hermite functions, the coefficients of which provide a low order feature vector which is characteristic of the texture. The Hermite coefficients for 240 hand-segmented regions of grass, trees, sky and river from 60 forward looking infrared (FLIR) images have been used to train and validate 2 neural networks, which have subsequently been used to label FLIR images segmented using co-occurrence techniques [1].