Enhancement of imagery of objects with highly dynamic brightness and large rotational motion.

We report on application of multi-frame super-resolution (SR) to sampling limited imagery that models space objects (SOs). The difficulties of multi-frame image processing of SOs include abrupt illumination changes and complex in scene SO motion. These conditions adversely affect the accuracy of motion estimation necessary for resolution enhancement. We analyze the motion estimation errors from the standpoint of an optical flow (OF) interpolation error metric and show dependence of the object tracking accuracy on brightness changes and on the pixel displacement values between subsequent images. Despite inaccuracies of motion estimation, we demonstrate spatial acuity enhancement of the pixel limited resolution of model SO motion imagery by applying a SR algorithm that accounts for OF errors. In addition to visual inspection, image resolution improvement attained in the experiments is assessed quantitatively; a 1.8× resolution enhancement is demonstrated.

Video rate nine-band multispectral short-wave infrared sensor.

Short-wave infrared (SWIR) imaging sensors are increasingly being used in surveillance and reconnaissance systems due to the reduced scatter in haze and the spectral response of materials over this wavelength range. Typically SWIR images have been provided either as full motion video from framing panchromatic systems or as spectral data cubes from line-scanning hyperspectral or multispectral systems. Here, we describe and characterize a system that bridges this divide, providing nine-band spectral images at 30 Hz. The system integrates a custom array of filters onto a commercial SWIR InGaAs array. We measure the filter placement and spectral response. We demonstrate a simple simulation technique to facilitate optimization of band selection for future sensors.

Spectral anomaly detection in deep shadows.

Although several hyperspectral anomaly detection algorithms have proven useful when illumination conditions provide for enough light, many of these same detection algorithms fail to perform well when shadows are also present. To date, no general approach to the problem has been demonstrated. In this paper, a novel hyperspectral anomaly detection algorithm that adapts the dimensionality of the spectral detection subspace to multiple illumination levels is described. The novel detection algorithm is applied to reflectance domain hyperspectral data that represents a variety of illumination conditions: well illuminated and poorly illuminated (i.e., shadowed). Detection results obtained for objects located in deep shadows and light-shadow transition areas suggest superiority of the novel algorithm over standard subspace RX detection.