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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 10, 2011
- Accession Number
- ADA550165
Entities
People
- A. M. Kim
- A. V. Kanaev
- B. J. Daniel
- J. G. Neumann
- Ko‐Tao Lee
Organizations
- United States Naval Research Laboratory