Neural network approach to the classification of urban images - Evans, Hywel F.J. (1996)
Over the past few years considerable research effort has been devoted to the study of pattern recognition methods applied to the classification of remotely sensed images. Neural network methods have been widely explored, and been shown to be generally superior to conventional statistical methods. However, the classification of objects shown on greylevel high resolution images in urban areas presents significant difficulties. This thesis presents the results of work aimed at reducing some of these difficulties. High resolution greylevel aerial images are used as the raw material, and methods of processing using neural networks are presented. If a per-pixel approach were used there would be only one input neuron, the pixel greylevel, which would not provide a sufficient basis for successful object identification. The use of spatial neighbourhoods providing an m x m input vector centred on each pixel is investigated; this method takes into account the texture of the pixel's neighbourhood.