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 i maging process, important features of the observed tridimensional scene, in cluding geological lineaments, are mapped into the resulting 2-D image as s harp radiation variations or ed,ne elements (edgels), Edgels are detected b y 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 con tinuous quasilinear features and can be described as a connected sequence o f 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 detect ors. We propose a cellular neural network (CNN) architecture to offer a large di rectional neighborhood to the lineament detection algorithm. The CNN uses a large circular neighborhood coupled with a directional-induced gradient fi eld to link together edgels with similar and continuous orientation. Missin g edgels are restored if a surrounding lineament is detected.