Laurie Gibson

IKONOS technical performance assessment

The worlds first high-resolution commercial satellite, IKONOS, was launched by Lockheed Martin for Space Imaging in September of 1999. The IKONOS satellite contains both a 1-meter 11-bit panchromatic sensor and a 4-band 4-meter 11-bit multispectral sensor. After launch a detailed On-Orbit Product Verification program was conducted to verify the IKONOS satellite and ground station products met all design specifications. This paper shares the results of the On-Orbit Product Verification program. Descriptions of the image quality attributes and a comparison between system requirements and On-orbit performance are included. The verified attributes are the Signal to Noise Ratio (SNR), Modulation Transfer Function (MTF), Band to Band Registration, and Radiometric and Geometric Accuracy. The Geometric Accuracy is examined with respect to all ground processed product requirements to produce monoscopic, stereo, orthorectified, and digital terrain matrix products. The result of this On-orbit testing and subsequent analyses show that all IKONOS system requirements have been met or exceeded.

Real-Time Measurement of Face Recognition in Rapid Serial Visual Presentation

Event-related potentials (ERPs) have been used extensively to study the processes involved in recognition memory. In particular, the early familiarity component of recognition has been linked to the FN400 (mid-frontal negative deflection between 300 and 500 ms), whereas the recollection component has been linked to a later positive deflection over the parietal cortex (500–800 ms). In this study, we measured the ERPs elicited by faces with varying degrees of familiarity. Participants viewed a continuous sequence of faces with either low (novel faces), medium (celebrity faces), or high (faces of friends and family) familiarity while performing a separate face-identification task. We found that the level of familiarity was significantly correlated with the magnitude of both the early and late recognition components. Additionally, by using a single-trial classification technique, applied to the entire evoked response, we were able to distinguish between familiar and unfamiliar faces with a high degree of accuracy. The classification of high versus low familiarly resulted in areas under the curve of up to 0.99 for some participants. Interestingly, our classifier model (a linear discriminant function) was developed using a completely separate object categorization task on a different population of participants.

Expert image analysts show enhanced visual processing in change detection

Expertise facilitates change detection performance, but the neural underpinnings of these benefits are unknown. Expert image analysts showed larger change-related ERP effects between about 100–200 msec after stimulus onset than did novices, which correlated with both accuracy and years of analysis experience. These results demonstrate that years of visual experience can induce fundamental changes in early visual processing which are related to change detection abilities.

Methodology study for development of a motion imagery quality metric

The motion imagery community would benefit from the availability of standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, requires modifications. The dynamic nature of motion imagery introduces a number of factors that do not affect the perceived interpretability of still imagery - namely target motion and camera motion. A set of studies sponsored by the National Geospatial-Intelligence Agency (NGA) have been conducted to understand and quantify the effects of critical factors. This study discusses the development and validation of a methodology that has been proposed for the development of a NIIRS-like scale for motion imagery. The methodology adapts the standard NIIRS development procedures to the softcopy exploitation environment and focuses on image interpretation tasks that target the dynamic nature of motion imagery. This paper describes the proposed methodology, presents the findings from a methodology assessment evaluation, and offers recommendations for the full development of a scale for motion imagery.

Integration of Automated Neural Processing into an Army-Relevant Multitasking Simulation Environment

Brain-computer interface technology has experienced a rapid evolution over recent years. Recent studies have demonstrated the feasibility of detecting the presence or absence of targets in visual imagery from the neural response alone. Classification accuracy persists even when the imagery is presented rapidly. While this capability offers significant promise for applications that require humans to process large volumes of imagery, it remains unclear how well this approach will translate to more real-world scenarios. To explore the viability of automated neural processing in an Army-relevant operational context, we designed and built a simulation environment based on a ground vehicle crewstation. Here, we describe the process of integrating and testing the automated neural processing capability within this simulation environment. Our results indicate the potential for significant benefits to be realized by incorporating brain-computer interface technology into future Army systems.

CASSIE: Contextual Analysis for Spectral and Spatial Information Extraction

Advances in understanding the biology of vision show that humans use not only bottom-up, feature-based information in visual analysis, but also top-down contextual information. To reflect this method of processing, we developed a technology called CASSIE for Science Applications International Corporation (SAIC) that uses low-level image features and contextual cues to determine the likelihood that a certain target will be found in a given area. CASSIE is a tool by which information from various data layers can be probabilistically combined to determine spatial and informational context within and across different types of data. It is built on a spatial foundation consisting of a two-dimensional hexagonal, hierarchical grid structure for data storage and access. This same structure facilitates very fast computation of information throughout the hierarchy for all data layers, as well as fast propagation of probabilistic information derived from those layers. Our research with CASSIE investigates the effectiveness of generated probability maps to reflect a human interpretation, potential benefits in terms of accuracy and processing speed for subsequent target detection, and methods for incorporating feedback from target detection algorithms to apply additional contextual constraints (for example, allowable or expected target groupings). We discuss further developments such as learning in CASSIE and how to incorporate additional data modalities.

User evaluation of differential compression for motion imagery

Motion imagery will play a critical role in future combat operations. The ability to provide a real time, dynamic view of the battlefield, as well as the capability to maintain persistent surveillance, together make motion imagery a valuable source of information for the soldier. Acquisition and exploitation of this rich source of information, however, depends on available communications bandwidth to transmit the necessary information to users. Methods for reducing bandwidth requirements include a variety of image compression and frame decimation techniques. This study explores spatially differential compression in which targets in the clips are losslessly compressed, while the background regions are highly compressed. This study evaluates the ability of users to perform standard target detection and identification tasks on the compressed product, compared to performance on uncompressed imagery or imagery compressed by other methods. The paper concludes with recommendations for future investigations.

Template decomposition and inversion over hexagonally sampled images

The family of real-valued circulant templates on nXm rectangular images is isomorphic to a quotient ring of the ring of real polynomials in two variables. Template decomposition is equivalent to factoring the corresponding polynomial. Template invertibility corresponds to polynomial invertibility in the quotient ring. Factoring and inverting are more difficult for polynomials in two variables than for those in one. Hexagonally sampled images have properties which simplify these operations. Hexagons organize themselves naturally into a hierarchy of snowflake-shaped regions. These tile the plane and consequently yield a simple definition of circulancy. Unlike the circulancy of rectangles in the plane, which yields a toroidal topology, the hexagonal analogue yields the topology of a circle. As a result, circulant templates are mapped isomorphically into a quotient of the ring of polynomials in one variable. These polynomials are products of linear factors over the complex numbers. A polynomial will be invertible in the quotient ring whenever each of its linear factors is invertible. This results in a simple criterion for template invertibility.