NASA Scientific and Technical Aerospace Reports

ground sampling distance, therefore, embedded in a single pixel. Under such a circumstance, these targets can be only detected
spectrally at the subpixel level, not spatially as ordinarily conducted by classical image processing techniques. This paper
investigates a more challenging issue than subpixel detection, which is the estimation of target size at the subpixel level. More
specifically, when a subpixel target is detected, we would like to know ‘what is the size of this particular target within the
pixel?’ The proposed approach is to estimate the abundance fraction of a subpixel target present in a pixel, then find what
portion it contributes to the pixel that can be used to determine the size of the subpixel target by multiplying the ground
sampling distance. In order to make our idea work, the subpixel target abundance fraction must be accurately estimated to truly
reflect the portion of a subpixel target occupied within a pixel. So, a fully constrained linear unmixing method is required to
reliably estimate the abundance fractions of a subpixel target for its size estimation. In this paper, a recently developed fully
constrained least squares linear unmixing is used for this purpose. Experiments are conducted to demonstrate the utility of the
proposed method in comparison with an unconstrained linear unmixing method, unconstrained least squares method, two
partially constrained least square linear unmixing methods, sum-to-one constrained least squares, and nonnegativity
constrained least squares.
DTIC
Detection; Estimates; Image Processing; Imagery; Least Squares Method; Remote Sensing; Targets
20060001835 North Carolina State Univ., Raleigh, NC USA
Sensitivity to Noise Variance in a Social Network Dynamics Model
Banks, H. T.; Karr, A. F.; Nguyen, H. K.; Samuels, J. R., Jr; Nov. 14, 2005; 16 pp.; In English
Contract(s)/Grant(s): AFOSR-FA9550-04-1-0220
Report No.(s): AD-A440344; No Copyright; Avail.: Defense Technical Information Center (DTIC)
The dynamics of social networks are modeled with a system of continuous Stochastic Ordinary Differential Equations
(SODE). With the proper amount of noise input, the SODE model captures dynamic features that are lacking in the
corresponding deterministic ODE model. Therefore, sensitivity to noise levels is investigated by considering four different
regimes: essentially deterministic, noise-enriched, noise-enlarged, and noise-dominated. Each regime is defined based on the
behavior of solutions of the SODE, and the geometry of the regimes is categorized with stochastic simulations.
DTIC
Differential Equations; Human Relations; Mathematical Models; Network Analysis; Sensitivity; Stochastic Processes
20060001852 Massachusetts Univ., Amherst, MA USA
Practical Markov Logic Containing First-Order Quantifiers With Application to Identity Uncertainty
Culotta, Aron; McCallum, Andrew; Sep. 8, 2005; 12 pp.; In English
Report No.(s): AD-A440385; No Copyright; Avail.: Defense Technical Information Center (DTIC)
Markov logic is a highly expressive language recently introduced to specify the connectivity of a Markov network using
first-order logic. While Markov logic is capable of constructing arbitrary first-order formulae over the data, the complexity of
these formulae is often limited in practice because of the size and connectivity of the resulting network. In this paper, we
present approximate inference and training methods that incrementally instantiate portions of the network as needed to enable
first-order existential and universal quantifiers in Markov logic networks. When applied to the problem of object identification,
this approach results in a conditional probabilistic model that can reason about objects, combining the expressively of recently
introduced BLOG models with the predictive power of conditional training. We validate our algorithms on the tasks of citation
matching and author disambiguation.
DTIC
Identities; Markov Processes
20060001857 Connecticut Univ., Storrs, CT USA
Robust Action Strategies to Induce Desired Effects
Tu, Haiying; Levchuk, Yuri N.; Pattipati, Krishna R.; Jan. 1, 2002; 23 pp.; In English; Original contains color illustrations
Contract(s)/Grant(s): N00014-00-1-0101
Report No.(s): AD-A440391; No Copyright; Avail.: Defense Technical Information Center (DTIC)
This paper provides a new methodology for obtaining a near-optimal strategy (i.e., specification of courses of action over
time) for achieving the desired effects in a mission environment that also is robust to environmental perturbations (i.e.,
unexpected events and/or parameter uncertainties). A dynamic Bayesian network (DBN)-based stochastic mission model is
employed to represent the dynamic and uncertain nature of the environment. Genetic algorithms are applied to search for a
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