Analyst Performance Measures. Volume 3. Information Quality Tools for Persistent Surveillanec Data Sets
Abstract
The Air Force desires a comprehensive vehicle to identify and address requirements for information quality tools and techniques to support defensive and offensive operations research in the layered sensing domain. As use of remote sensors in the Air and Space domains increases, the value of the sensor datasets must be maximized, and assurances established that the product outcomes meet the application requirements. As multiple sensors are combined into layered sensing systems, this increases the need to understand not only the quality and fitness for using the individual sensor data streams, but also how to assess the quality and value of the aggregate data. The scope of this task order is to develop metrics that assess the quality and effectiveness of persistent surveillance data sets. The project also explores the use of three dimensional (3D) visualization in rendering layered data sets and experiments with the integration of textual information. In addition, integrating processing of data available from multiple types of sensors (such as in a Smart Environment) has been explored, and experiments have been done to support data fusion for multiple sensors. Two novel image quality metrics, Saliency-Based Structural Similarity Index (S-SSIM) and Saliency-Based Visual Information Fidelity (S-VIF) in pixel domain, were developed. The metrics are based on frequency-tuned, salient region detection and computationally inexpensive.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2011
- Accession Number
- ADA567798
Entities
People
- Brian Tsou
- John Talburt
- Mariofanna Milanova
- Mihail Tudoreanu
- Serhan Dagtas
Organizations
- University of Arkansas at Little Rock