Cellular neural network for automated detection of geological lineaments on Radarsat images

The analysis of natural linear structures, termed “lineaments” in satellite images, provides important information to the geologist. In the satellite imaging process, important features of the observed tridimensional scene, including geological lineaments, are mapped into the resulting 2D image as sharp radiation variations or edge elements (edgels). Edgels are detected by a first-order differentiation operator and are linked together with those in the vicinity on a basis of orientation continuity. Lineaments are mapped into remotely sensed satellite images as long and continuous quasilinear features and can be described as a connected sequence of edgels whose direction may change gradually along the sequence. Parts of the same lineament can be occluded by geomorphological features and must be linked together, a major drawback with local and small neighborhood detectors. The authors propose a cellular neural network (CNN) architecture to offer a large directional neighborhood to the lineament detection algorithm. The CNN uses a large circular neighborhood coupled with a directional-induced gradient field to link together edgels with similar and continuous orientation. Missing edgels are restored if a surrounding lineament is detected