Ramakalavathi Marapareddy and Sowmya Wilson Saripalli, University of Southern Mississippi Hattiesburg, USA

Remote sensing data gives the essential and critical information for detecting or identifying an object, a place, image fusion, change detection, and land cover classification of selected area of interest. The runway detection is an important topic because of its applications in military and civil aviation fields. This paper presents an approach for runway detection using Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVAR) data by implementing K-means clustering method. The obtained results reveal that we can obtain better detection, for the 9 and 11 classes, with iterations set to 10. In this work, the effectiveness of algorithm was demonstrated using quad polarimetric L-band Polarimetric Synthetic Aperture Radar(polSAR) imagery from NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is Louis Armstrong New Orleans International Airport, LA, USA.

Remote sensing, Runway detection, K-means clustering, polSAR