Neural classification of high resolution remote sensing imagery for power transmission lines surveillance

The larger availability of high resolution remotely sensed data, provided by novel aircraft and space sensors, offers new perspective to image processing techniques, but it introduces also the need for operational classification tools in order to completely exploit the potentialities of these data In many applicative contexts, in particular for technological network surveillance tasks, which involve specific requirements, such as (1) high resolution and accuracy in object recognition and positioning; (2) straightforward update and change detection; (3) geographic generalisation. The application presented deals with the recognition of features of interest for the surveillance of power transmission lines using IKONOS imagery. We proposed a methodology in which multi-scale and neural techniques are synergically combined to identify features at different scales and to fuse them for class discrimination. The results obtained on a pilot area in Northern Italy proved that the combination of multi-window feature extraction and neural soft classification produced an agile and flexible model that can act as a classifier of objects that vary in shape, size and structure.