Fully automatic detection, feature extraction and classification of obstacles to air navigation

Abstract

Correct identification of obstacles at the periphery of airports is an important issue to ensure safe takeoff, flight, and landing to aircrafts. This work is carried on as part of the obstacle risk assessment and risk mitigation operations in the aviation security framework. This paper presents a novel fully automatic remote sensing methodology for the detection, shape and signature extraction and classification of obstacles to air navigation from very high resolution (VHR) multispectral (MS) satellite stereo couples images, here defined feature extraction (FE). In order to reduce the costs, the proposed technique is applied only on detailed areas where orographic/topographic changes potentially associated with variations in the obstacles to air navigation in wide areas have been previously detected through a low-cost pre-screening change detection (CD) methodology applied to cheaper high resolution (HR) satellite imagery. The combination of CD and FE strategies offers a low-cost and fast solution to the problem of updating airport obstacle chart.