Automated registration has been pursued for decades within academia, government, and commercial
sectors as a fundamental enabling technology to support improved positioning, automated change
detection, target recognition, and multi-source fusion. The focus of previous and current research has
largely been on automated image-to-image registration tools. Comparatively little attention has been paid to
automated registration of non-raster data (e.g., vector) stored within Geographical Information Systems
(GIS) or other types of databases.
The Vision Inspired Spatial Engine (VISE) is an innovative approach to automated registration. Rather
than focusing on automated registration of a specific data source such as imagery, VISE uses a novel
object-matching paradigm which is independent of data source. VISE assesses the fuzzy spatial similarity
between two or more object patterns that can be of different shape or size by use of a top-down multiple
resolution approach that simultaneously optimizes both edge and area match between vector-represented
spatial features. As a by-product of the VISE pattern-matching process, object-to-object and object-to-image
registration between different data sources are possible.
This paper demonstrates the VISE technology applied toward the automated registration and object-level
correlation of Hyperspectral (HSI), LIDAR and Electro-Optical (EO) Imagery and derived objects, and
other GIS data sources.
Automated target recognition (ATR) methods hold promise for rapid extraction of critical information from imagery data to support military missions. Development of ATR tools generally requires large amounts of imagery data to develop and test algorithms. Deployment of operational ATR systems requires performance validation using operationally relevant imagery. For early algorithm development, however, restrictions on access to such data is a significant impediment, especially for the academic research community. To address this limitation, we have developed a set of grayscale imagery as a surrogate for panchromatic imagery that would be acquired from airborne sensors. This surrogate data set consists of imagery of ground order of battle (GOB) targets in an arid environment. The data set was developed by imaging scale models of these targets set in a scale model background. The imagery spans a range of operating conditions and provides a useful image set for initial explorations of new approaches for ATR development.
SAIC in support of the National Geospatial-Intelligence Agency (NGA) Synergistic Targeting Auto-Extraction and Registration (STAR) Program is conducting an evaluation of several automated registration applications developed by different vendors. A common problem when attempting to compare multiple automated registration packages is interpreting the results from the different applications in a consistent manner. Different vendors use different matching and adjustment methods as well as different output formats to store the results from automated registration. It is nearly impossible, due to the cost in terms of labor and time, to convert the results from different vendors into a common format or framework for evaluation. The approach taken by the STAR Program is to separate the automated registration process into three separate but equal components: automated data matching, data adjustment, and output format used to store the adjusted results. The results of different vendor's automated matching process will be fed into a single, common weighted least squares package for adjustment. The use of a common physics based adjustment process will allow the STAR Program Office to evaluate the results from several different automated registration applications in a consistent and fair manner. The metrics collected during the STAR Automated Registration Evaluation will include timing statistics, matching statistics, and geospatial accuracy statistics from comparisons to GPS survey sites. In this paper, we present the STAR automated registration evaluation architecture. Over the course of the next year the STAR Program Office will develop and then use this architecture to evaluate several different automated registration algorithms.