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Proceedings Paper

Vision inspired spatial engine (VISE): automated object registration for multisource fusion
Author(s): Derek Lewis; Dan Edwards; Joseph Hufnagel; Melissa Kim
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Paper Abstract

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.

Paper Details

Date Published: 12 May 2010
PDF: 12 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769518 (12 May 2010); doi: 10.1117/12.851706
Show Author Affiliations
Derek Lewis, National Geospatial-Intelligence Agency (United States)
Dan Edwards, National Geospatial-Intelligence Agency (United States)
Joseph Hufnagel, National Geospatial-Intelligence Agency (United States)
Melissa Kim, National Geospatial-Intelligence Agency (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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