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.