Using Data Fusion to Detect, Characterize and Monitor Underground Facilities: This project explored the potential of data fusion for the detection of underground facilities used to develop, build and test weapons of mass destruction. It exploited the diverse data sets collected by DOEs Airborne Multisensor Pod System (AMPS) over tunnels at the Semipalatinsk Russian nuclear test site (a direct analog the U.S. Nevada Test Site). Concurrent data sets provided by AMPS included: Radar, several optical systems(CASI, Daedalus, Probe), and high resolution film products (RC-30). The project demonstrated the how these data sets could be combined to improve the detection, characterization and monitoring of an underground site. For more information see: : "Petrie, GM, KL Steinmaus, and GG He (1998) Remote Detection of Underground Facilities: A Case Study Using Multisensor Data from the U.S. Department of Energy's Airborne Multisensor Pod System (AMPS), US Department of Energy Office of Nonproliferation and National Security Project Report, PNNL-12049, Pacific Northwest National Laboratory, Richland, WA, 28 pages.":
River AFE for FFD: To help meet demanding geospatial map generating requirements for NIMA this project developed computer-assisted methodologies to extract rivers. The project used a multidata and multidisciplinary approach that included: remote sensing, geology, meteorology, artificial intelligence and vision technology; however, main empathies was on fusing methodology from image processing and hydrologic modeling. For more information see: : "Petrie G.M. et. al.. "Multidata Analysis for Automatic River Extraction" Remote Sensing and Hydrology 2000, page 427-432. Editors: Manfred Owe, Kaye Brubaker, Jerry Richie and Albert Rango":
Change Detection For Broad Area Search:: An important concern is monitoring very broad areas of the earth's surface, with diverse physical and cultural characteristics, for consequential changes over time. Limited budget, human resources, and time can further complicate this inherently difficult effort. To help meet these challenges, this project developed a remote sensing change detection methodology that is optimized for broad area search. This methodology exploits not only the remotely-measured spectral changes, but also the context provided by GIS information. Its development was motivated by previous work which strongly suggested that by synergistically combining `orthogonal' spatial and spectral information it would be possible to greatly increase the identification and characterization of subtle changes. For more information see: : "Petrie, G.M., and E.M. Perry. 1999. Large area change detection optimized with spatial attributes. 3rd World Multiconference on Systemics, Cybernetics and Informatics, Jul 31 - Aug 4 1999, Orlando FL.":
Exploiting Distributed Computing for Remote Sensing data Sets: Processing image data generated by new remote sensing systems can severely tax the computational limits of the classic single processor systems that are normally available to the remote sensing practitioner. Operating on these large data sets with a single computer system sometimes means that simplifying approximations are used that can limit the precision of the final results. While this assumption has the advantage of greatly reducing the amount of pixels that must be processed this abstraction can also mask important structures in the raw data. Recent work by this project strongly suggests that a distributed network of inexpensive PCs can be designed that is optimal to deal with the type of computationally intensive problems encountered in processing remotely sensed images. For more information see: : " Petrie G., et. al. "Distributed Computing Approach for Remote sensing data" In Proceedings of the 34th Interface Symposium. April 17-20, 2002(in press). :
Extracting Subtle Ecological Information with Remote Sensing: This project is developing and demonstrating methodology for BLM to help them characterize and manage extremely large areas of rangeland in a timely and cost effective manner. This methodology exploits new trends not only in remote sensing (e.g. new satellite systems) but also takes advantage of new advancements in GIS, GPS, communications and computer science. For more information see: : "Petrie .G.M. et. al. "Large Area Monitoring Using Spatial Data Fusion". Proceedings of the Third International Conference on Geospatial` Information in Agriculture and Forestry November 2001":
Virtual Satellite Imagery: One ongoing goal is to develop new methodology that combines different satellite imagery into a new `virtual' image that provides new information. A recent example a successfully application of this strategy is the development of methodology for image resolution enhancement which takes advantage of the spatial relationship between high- and low-resolution images within an area of overlap. An example would to use the spatial information in the area of overlap between a small IKONOS image and SPOT image to sharpen the much larger SPOT image. . For more information see: : "Petrie .G.M. et. al. "Inverse kriging to enhance spatial resolution of imagery", Proceedings of the SPIE 47th Annual Meeting(in press).":