Automated object recognition and pattern matching analysis of runways using surface track data

Accurate and timely information is critical for the safe landing of aircraft in now-a-days. The goal of an EMAS (Engineered Materials Arresting System) is to avoid aircraft overrun with no human injury and minimal aircraft damage. Although many techniques have been developed for the analysis of object detection, relatively few researchers have considered image analysis as an aid to aircraft landing. In one system, image intensity edges are used to detect the sides of a runway in an image sequence, and the 3-dimensional position and orientation of the runway can be estimated. For better detection and extraction of objects from an aerial image, a fuzzy network system is used. In another system integration of biologically and geometrically inspired approaches for detecting objects from hyper spectral and/or multispectral (HS/MS), multi-scale, multiplatform imagery is used. But the drawback of these technologies is similarity in the top view of runways and building and roads or other objects. We propose a new method to detect and track the runway using pattern matching and texture analysis using digital images from cameras mounted on the aircraft. In order to detect runway from aerial image edge detection algorithms are used. In this paper the edge detection techniques used are Hough Transform, Canny Filter and Sobel Filter algorithms which lead to efficient detection of runways from aerial images.