Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
5-2-2008
Abstract
The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and compared against existing vector layer(s) to be registered. Given that available automated feature extraction (AFE) methods quite often produce false features and miss some features, we use additional information to improve AFE. This information is the existing vector data, but the vector data are not perfect as well. To deal with this problem the VRR process uses an algebraic structural algorithm (ASA), similarity transformation of local features algorithm (STLF), and a multi-loop process that repeats (AFE-VRR) process several times. The experiments show that it was successful in registering road vectors to commercial panchromatic and multi-spectral imagery.
Recommended Citation
Kovalerchuk, B., Doucette, P., Seedahmed, G., Brigantic, R., Kovalerchuk, M., & Graff, B. (2008). Automated vector-to-raster image registration. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660W. https://doi.org/10.1117/12.778431
Journal
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Rights
© 2008 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)
Comments
Copyright 2008 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
This article was originally published in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV. The full-text article from the publisher can be found here.