Remote Ultra-Low Light Imaging detectors are photon limited detectors developed at Los Alamos National
Laboratories. RULLI detectors provide a very high degree of temporal resolution for the arrival times of detected photoevents,
but saturate at a photo-detection rate of about 10<sup>6</sup> photo-events per second. Rather than recording a conventional
image, such as output by a charged coupled device (CCD) camera, the RULLI detector outputs a data stream consisting
of the two-dimensional location, and time of arrival of each detected photo-electron. Hence, there is no need to select a
specific exposure time to accumulate photo-events prior to the data collection with a RULLI detector - this quantity can
be optimized in post processing. RULLI detectors have lower peak quantum efficiency (from as low as 5% to perhaps as
much as 40% with modern photocathode technology) than back-illuminated CCD's (80% or higher). As a result of these
factors, and the associated analyses of signal and noise, we have found that RULLI detectors can play two key new roles
in SSA: passive imaging of exceedingly dim objects, and three-dimensional imaging of objects illuminated with an
appropriate pulsed laser. In this paper we describe the RULLI detection model, compare it to a conventional CCD
detection model, and present analytic and simulation results to show the limits of performance of RULLI detectors used
for SSA applications at AMOS field site.
The hyperspectral subpixel detection and classification problem has been intensely studied in the downward-looking case, typically satellite imagery of agricultural and urban areas. In contrast, the hyperspectral imaging case when "looking up" at small or distant satellites creates new and unforeseen problems. Usually one pixel or one fraction of a pixel contains the imaging target, and spectra tend to be time-series data of a single object collected over some time period under possibly varying weather conditions; there is little spatial information available. Often, the number of collected traces is less than the number of wavelength bins, and a materials database with imperfect representative spectra must be used in the subpixel classification and unmixing process. A procedure is formulated for generating a "good" set of classes from experimentally collected spectra by assuming a Gaussian distribution in the angle-space of the spectra. Specifically, Kernel K-means, a suboptimal ML-estimator, is used to generate a set of classes. Covariance information from the resulting classes and weighted least squares methods are then applied to solve the linear unmixing problem. We show with cross-validation that Kernel K-means separation of laboratory material classes into "smaller" virtual classes before unmixing improves the performance of weighted least squares methods.