Runway Detection Using Classification Based on Polarimetric Decomposed Features
Computing Sciences and Computer Engineering
© 2017 IEEE. Current developments in the arena of remote sensing technology has opened innovative prospects and impelled the way of analyzing images from remote sensing satellites to detect or identify an object, or a place, which is selected as an area of interest. The detection of airport becomes a motivating topic recently because of its applications and importance in military and civil aviation fields. This paper presents an approach for airport detection using remote sensing images by implementing unsupervised classification techniques based on polarimetric decomposed features that include Entropy, Eigenvalue parameters, and Alpha angle. The obtained results reveal that classification based on decomposed polarimetric features provides better results for the detection of the target. The classification based Eigenvalue parameter 2 gives superior results compared to Eigenvalue parameter 1 and 3. In this work, the effectiveness of the algorithms was demonstrated using quad-polarimetric L-band Polarimetric Synthetic Aperture Radar (poISAR) imagery from the NASA Jet Propulsion Laboratory's (JPL's) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is Louis Armstrong New Orleans International Airport, Louisiana, USA.
2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017
(2018). Runway Detection Using Classification Based on Polarimetric Decomposed Features. 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017.
Available at: https://aquila.usm.edu/fac_pubs/18118