Remote sensing and data fusion of cultural and physical landscapes

This dissertation is written as part of the three-article option offered by the Geography Department at UNC Greensboro. Each article addresses specific research issues within Remote Sensing, Photogrammetry, and three-dimensional modeling related structural and subsurface remote sensing of historic cultural landscapes. The articles submitted in this dissertation are both separate study sites and research questions, but the unifying theme of geographic research methods applies throughout. The first article is titled Terrestrial Lidar and GPR Investigations into the Third Line of Battle at Guilford Courthouse National Military Park, Guilford County, North Carolina is published in the book Digital Methods and Remote Sensing in Archaeology: Archaeology in the Age of Sensing. Forte, Maurizio, Campana, Stefano R.L. (Eds.) 2016. The results of the research demonstrate the successful exportation of GPR data into three-dimensional point clouds. Subsequently, the converted GPR points in conjunction with the TLS were explored to aid in the identification of the colonial subsurface. The second article submitted for consideration is titled “Three-Dimensional Modeling using Terrestrial LiDAR, Unmanned Aerial Vehicles, and Digital Cameras at House in the Horseshoe State Historic Site, Sanford, North Carolina.” There are two different research components to this study, modeling a structure and the landscape. The structure modeling section compares three different remote sensing approaches to the capture and three-dimensional model creation of a historic building. A detailed comparison is made between the photogrammetric models generated from digital camera photography, a terrestrial laser scanner (TLS) and an unmanned aerial vehicle (UAS). The final article, “Geophysical Investigations at the Harper House Bentonville Battlefield, NC State Historic Site” submitted focuses on the Harper House located in at the Bentonville Civil War battlefield. UNCG conducted a geophysical survey using a ground penetrating radar and gradiometer. The findings from the data were used to determine and pinpoint areas of interest for subsequent excavation

Horizontal Fusion: Enabling Net-Centric Operations and Warfare

the ability to access real-time information at the right time to make the right decisions

An intelligent situation awareness support system for safety-critical environments

Operators handling abnormal situations in safety-critical environments need to be supported from a cognitive perspective to reduce their workload, stress, and consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing error. However, existing system safety researches focus mainly on technical issues and often neglect SA. This study presents an innovative cognition-driven decision support system called the situation awareness support system (SASS) to manage abnormal situations in safety-critical environments in which the effect of situational complexity on human decision-makers is a concern. To achieve this objective, a situational network modeling process and a situation assessment model that exploits the specific capabilities of dynamic Bayesian networks and risk indicators are first proposed. The SASS is then developed and consists of four major elements: 1) a situation data collection component that provides the current state of the observable variables based on online conditions and monitoring systems, 2) a situation assessment component based on dynamic Bayesian networks (DBN) to model the hazardous situations in a situational network and a fuzzy risk estimation method to generate the assessment result, 3) a situation recovery component that provides a basis for decision-making to reduce the risk level of situations to an acceptable level, and 4) a human-computer interface. The SASS is partially evaluated by a sensitivity analysis, which is carried out to validate DBN-based situational networks, and SA measurements are suggested for a full evaluation of the proposed system. The performance of the SASS is demonstrated by a case taken from US Chemical Safety Board reports, and the results demonstrate that the SASS provides a useful graphical, mathematically consistent system for dealing with incomplete and uncertain information to help operators maintain the risk of dynamic situations at an acceptable level. © 2014 Elsevier B.V. All rights reserved

Swarm Based Implementation of a Virtual Distributed Database System in a Sensor Network

The deployment of unmanned aerial vehicles (UAVs) in recent military operations has had success in carrying out surveillance and combat missions in sensitive areas. An area of intense research on UAVs has been on controlling a group of small-sized UAVs to carry out reconnaissance missions normally undertaken by large UAVs such as Predator or Global Hawk. A control strategy for coordinating the UAV movements of such a group of UAVs adopts the bio-inspired swarm model to produce autonomous group behavior. This research proposes establishing a distributed database system on a group of swarming UAVs, providing for data storage during a reconnaissance mission. A distributed database system model is simulated treating each UAV as a distributed database site connected by a wireless network. In this model, each UAV carries a sensor and communicates to a command center when queried. Drawing equivalence to a sensor network, the network of UAVs poses as a dynamic ad-hoc sensor network. The distributed database system based on a swarm of UAVs is tested against a set of reconnaissance test suites with respect to evaluating system performance. The design of experiments focuses on the effects of varying the query input and types of swarming UAVs on overall system performance. The results show that the topology of the UAVs has a distinct impact on the output of the sensor database. The experiments measuring system delays also confirm the expectation that in a distributed system, inter-node communication costs outweigh processing costs

An improved method for text summarization using lexical chains

This work is directed toward the creation of a system for automatically sum-marizing documents by extracting selected sentences. Several heuristics including position, cue words, and title words are used in conjunction with lexical chain in-formation to create a salience function that is used to rank sentences for extraction. Compiler technology, including the Flex and Bison tools, is used to create the AutoExtract summarizer that extracts and combines this information from the raw text. The WordNet database is used for the creation of the lexical chains. The AutoExtract summarizer performed better than the Microsoft Word97 AutoSummarize tool and the Sinope commercial summarizer in tests against ideal extracts and in tests judged by humans

Framework for optimizing intelligence collection requirements

In the military, typical mission execution goes through cycles of intelligence collection and action planning phases. For complex operations where many parameters affect the outcomes of the mission, several steps may be taken for intelligence collection before the optimal Course of Action is actually carried out. Human analytics suggests the steps of: (1) anticipating plausible futures, (2) determining information requirements, and (3) optimize the choice of feasible and cost-effective intelligence requirements. This work formalizes this process by developing a decision support tool to determine information requirements needed to differentiate critical plausible futures, and formulating a mixed integer programming problem to trade-off the feasibility and benefits of intelligence collection requirements. Course of Action planning has been widely studied in the military domain, but mostly in an abstract fashion. Intelligence collection, while intuitively aiming at reducing uncertainties, should ultimately produce optimal outcomes for mission success. Building on previous efforts, this work studies the effect of plausible futures estimated based on current adversary activities. A set of differentiating event attributes are derived for each set of high impact futures, forming a candidate collection requirement action. The candidate collection requirement actions are then used as inputs to a Mixed Integer Programming formulation, which optimizes the plausible future mission state subject to timing and cost constraints. The plausible future mission state is estimated by assuming that the Collection Requirement Actions can potentially avert the damages adversary future activities might cause. A case study was performed to demonstrate several use cases for the overall framework

Cognitively-Engineered Multisensor Data Fusion Systems for Military Applications

The fusion of imagery from multiple sensors is a field of research that has been gaining prominence in the scientific community in recent years. The technical aspects of combining multisensory information have been and are currently being studied extensively. However, the cognitive aspects of multisensor data fusion have not received so much attention. Prior research in the field of cognitive engineering has shown that the cognitive aspects of any human-machine system should be taken into consideration in order to achieve systems that are both safe and useful. The goal of this research was to model how humans interpret multisensory data, and to evaluate the value of a cognitively-engineered multisensory data fusion system as an effective, time-saving means of presenting information in high- stress situations. Specifically, this research used principles from cognitive engineering to design, implement, and evaluate a multisensor data fusion system for pilots in high-stress situations. Two preliminary studies were performed, and concurrent protocol analysis was conducted to determine how humans interpret and mentally fuse information from multiple sensors in both low- and high-stress environments. This information was used to develop a model for human processing of information from multiple data sources. This model was then implemented in the development of algorithms for fusing imagery from several disparate sensors (visible and infrared). The model and the system as a whole were empirically evaluated in an experiment with fighter pilots in a simulated combat environment. The results show that the model is an accurate depiction of how humans interpret information from multiple disparate sensors, and that the algorithms show promise for assisting fighter pilots in quicker and more accurate target identification