A robust automatic recognition system of man-made areas using morphological segmentation and very high resolution remotely sensed data

Imagery from the new generation very high-resolution sensors, will increase dramatically the geometric scene resolution but it will also decrease the accuracy of the For urban applications in particular, with the spatial properties of the new sensors it will be possible to recognise not only a generic texture window with specific urban characteristics, but also to detect in detail the objects that constitute the "urban theme". In this paper a segment based segmentation procedure is presented, based on the gray-scale geodesic morphological transformation and has been successfully utilised to detect built-up objects using only the 5m spatial resolution panchromatic data of the IRS1-C satellite. The imagery is subsequently classified on a segment basis using a multi-layer perceptron neural network classifier.