title: Object level HSI-LIDAR data fusion for automated detection of difficult targets.
abstract: Data fusion from disparate sensors significantly improves automated man-made target detection performance compared to that of just an individual sensor. In particular, it can solve hyperspectral imagery (HSI) detection problems pertaining to low-radiance man-made objects and objects in shadows. We present an algorithm that fuses HSI and LIDAR data for automated detection of man-made objects. LIDAR is used to define a set of potential targets based on physical dimensions, and HSI is then used to discriminate between man-made and natural objects. The discrimination technique is a novel HSI detection concept that uses an HSI detection score localization metric capable of distinguishing between wide-area score distributions inherent to natural objects and highly localized score distributions indicative of man-made targets. A typical man-made localization score was found to be around 0.5 compared to natural background typical localization scores being less than 0.1.
authors: Naval Research Laboratory, 4555 Overlook Avenue, Washington, DC 20375, USA.
authors: A V
authors: AV
authors: Kanaev
authors: B J
authors: BJ
authors: Daniel
authors: J G
authors: JG
authors: Neumann
authors: A M
authors: AM
authors: Kim
authors: K R
authors: KR
authors: Lee
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D001330:Electronic Data Processing; D019277:Entropy; D015233:Models, Statistical; D055095:Optics and Photonics; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software