Automated road network extraction using artificial neural network

Abstract

Road detection from satellite images can be considered as a classification process in which pixels are divided into road and background classes and can be used as a criterion in road extraction process to discriminate between road and non road pixels. Apart from the spectral information, textural parameters and contextual information are usually used by human being in object recognition from images. Contributing texture information in the neural network input parameters seems to be an improving idea for road detection from satellite images. Different texture parameters show different aspects of textural behaviour in a defined neighbourhood of a given pixel. Artificial neural networks are found to be superior to several previous techniques due in part to their ability to incorporate both spectral and contextual information. In this paper, Neural Networks are applied on high resolution satellite images for road detection. At first, road detection has been performed using only spectral information. Then different texture parameters including contrast, energy, entropy and homogeneity are computed for each pixel using gray level co-occurrence matrix (GLCM) from source image and a pre-classified road raster map is produced. To optimize neural networks' functionality and to evaluate the impact of contributing texture parameters in road detection, extracted texture parameters are integrated with the spectral information.