Title :
Vessel classification on UAVs using inertial data and IR imagery
Author :
Demir, H. Seckin ; Akagunduz, Erdem ; Kubilay Pakin, S.
Author_Institution :
ASELSAN, MGEO, Ankara, Turkey
Abstract :
In this study, a civilian ship dataset is constructed via images captured by an infrared camera on an unmanned flying vehicle. By using this dataset and synchronized inertial data (UAV altitude and orientation, gimbal angles), a vessel classification method is proposed. The method first calculates the ship base length in meters by using segmented ship image and inertial data. By fusing the descriptors obtained from the segmented ship images and estimated ship base length, vessel classification is performed.
Keywords :
autonomous aerial vehicles; image classification; image segmentation; infrared imaging; IR imagery; UAV; civilian ship dataset; infrared camera; segmented ship image; ship base length; synchronized inertial data; unmanned flying vehicle; vessel classification; Augmented reality; Cameras; Computer vision; Histograms; Image segmentation; Marine vehicles; Vehicles; inertial data; infrared imaging; vessel classification;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7129869