Automated Map Feature Vectorization with NOAA's Raster Nautical Chart

Award Information
Agency:
Department of Commerce
Branch
n/a
Amount:
$49,999.00
Award Year:
1997
Program:
SBIR
Phase:
Phase I
Contract:
n/a
Award Id:
37768
Agency Tracking Number:
37768
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
84 Oak Street, Binghamton, NY, 13905
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
J. Ching-yeng Huang
() -
Business Contact:
(607) 722-7803
Research Institute:
n/a
Abstract
For this SBIR effort, first, we will prove that map features in NOAA's raster nautical charts can be extracted with automated means rather than manual digitizing. Second, we will prove that the extracted raster-based features can be vetorized with no or insignificant error in size, shape, and spacing among objects, yielding a robust raster/vector hybrid spatial database. Third, using a comparative analysis we will prove that while automatic feature extraction and vectorization can also be achieved by using a set of conventional image processing and GIS software, these ad hoc procedures are cumbersome, inefficient, ineffective, and error-prone as compared to the proposed SRE system that integrates automatic feature extraction, raster-to-vector conversion and vector-to-raster conversion into one unified information processing environment. For testing against the SRE system composed of the IMaG, Vectorize and Rasterizer, we will select XV, IDRISI, ENVI, ARC/INFO as its counterpart. The potential benefits will include savings in labor costs by a significant margin of up to 50 percent, and improved efficiency, effectiveness and accuracy in vector database generation.

* information listed above is at the time of submission.

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