Integration, Testing, And Analysis Of Multispectral Imager On Small Unmanned Aerial System For Skin Detection

Small Unmanned Aerial Systems (SUAS) have been utilized by the military, geological researchers, and first responders, to provide information about the environment in real time. Hyperspectral Imagery (HSI) provides high resolution data in the spatial and spectral dimension; all objects, including skin have unique spectral signatures. However, little research has been done to integrate HSI into SUAS due to their cost and form factor. Multispectral Imagery (MSI) has proven capable of dismount detection with several distinct wavelengths. This research proposes a spectral imaging system that can detect dismounts on SUAS. Also, factors that pertain to accurate dismount detection with an SUAS are explored. Dismount skin detection from an aerial platform also has an inherent difficulty compared to ground-based platforms. Computer vision registration, stereo camera calibration, and geolocation from autopilot telemetry are utilized to design a dismount detection platform with the Systems Engineering methodology. An average 5.112% difference in ROC AUC values that compared a line scan spectral imager to the prototype area scan imager was recorded. Results indicated that an SUAS-based Spectral Imagers are capable tools in dismount detection protocols. Deficiencies associated with the test expedient prototype are discussed and recommendations for further improvements are provided

Application of the Augmented Operator Function Model for Developing Performance Metrics in Persistent Surveillance

Difficulties with the implementation of persistent Wide Area Motion Imagery (WAMI) sensors to support real-time military missions have risen within Intelligence, Surveillance, and Reconnaissance organizations. In this study, cognitive models were developed of operators performing real-time missions currently supported by narrow field of view Full Motion Video (FMV) and WAMI sensors. These models were used in conjunction with a cognitive task analysis, creating an augmented operator function model (OFM-COG). This thesis describes the OFM-COG and demonstrates how this model-based analysis technique can document the cognitive implications of persistent surveillance with motion imagery. The analytic procedures required to build this model result in a methodology for the definition of an information display system specific for intelligence analysis tasks. Specifically, the models developed examine the cognitive demands of an Imagery Analyst (IA) during a real-time mission, with WAMI and/or FMV. From this, a set of cognitive metrics for analyst performance were identified for the real-time military missions in persistent surveillance