Object level HSI-LIDAR data fusion for automated detection of difficult targets

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.

Longitudinal density modulation and energy conversion in intense beams

Density modulation of charged particle beams may occur as a consequence of deliberate action, or may occur inadvertently because of imperfections in the particle source or acceleration method. In the case of intense beams, where space charge and external focusing govern the beam dynamics, density modulation may, under some circumstances, be converted to velocity modulation, with a corresponding conversion of potential energy to kinetic energy. Whether this will occur depends on the properties of the beam and the initial modulation. This paper describes the evolution of discrete and continuous density modulations on intense beams and discusses three recent experiments related to the dynamics of density-modulated electron beams.