Edge Detection Using Adaptive-Neuro-Fuzzy-Interference-System in Remote Sensing Images

Edge detection is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. The need of edge detection is to find the discontinuities in depth, discontinuities in surface orientation, changes in material properties and variations in scene illumination. Remote sensing images are generally corrupted from noise. Mathematical morphology is a new technique for edge detection. It is a theory and technique for analysis and processing of geometrical structures, based on set theory. Mathematical morphology was originally developed for binary images, and later extends to grey scale functions and images. Basically the noise can be easily suppressed by mathematical morphology. So by using mathematical morphology the image can be enhanced and the edges can be detected. The result of edge detection using mathematical morphology will be compared with Sobel edge detector, Prewitt edge detector, laplacian of gaussian edge detector and Canny edge detector.