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Title: Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing.

Abstract

We describe a method for indexing and retrieving high-resolution image regions in large geospatial data libraries. An automated feature extraction method is used that generates a unique and specific structural description of each segment of a tessellated input image file. These tessellated regions are then merged into similar groups, or sub-regions, and indexed to provide flexible and varied retrieval in a query-by-example environment. The methods of tessellation, feature extraction, sub-region clustering, indexing, and retrieval are described and demonstrated using a geospatial library representing a 153 km2 region of land in East Tennessee at 0.5 m per pixel resolution.

Authors:
 [1];  [1];  [1];  [2];  [1];  [1];  [1];  [1]
+ Show Author Affiliations
  1. ORNL
  2. Oak Ridge National Laboratory (ORNL)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
931591
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: Photogrammetric Engineering & Remote Sensing; Journal Volume: 72; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; INFORMATION RETRIEVAL; IMAGES; AUTOMATION; TENNESSEE; GEOGRAPHIC INFORMATION SYSTEMS

Citation Formats

Tobin Jr, Kenneth William, Bhaduri, Budhendra L, Bright, Eddie A, Cheriydat, Anil, Karnowski, Thomas Paul, Palathingal, Paul J, Potok, Thomas E, and Price, Jeffery R. Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing.. United States: N. p., 2006. Web. doi:10.14358/PERS.72.5.531.
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Tobin Jr, Kenneth William, Bhaduri, Budhendra L, Bright, Eddie A, Cheriydat, Anil, Karnowski, Thomas Paul, Palathingal, Paul J, Potok, Thomas E, & Price, Jeffery R. Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing.. United States. doi:10.14358/PERS.72.5.531.
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Tobin Jr, Kenneth William, Bhaduri, Budhendra L, Bright, Eddie A, Cheriydat, Anil, Karnowski, Thomas Paul, Palathingal, Paul J, Potok, Thomas E, and Price, Jeffery R. Mon . "Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing.". United States. doi:10.14358/PERS.72.5.531.
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@article{osti_931591,
title = {Automated Feature Generation in Large-Scale Geospatial Libraries for Content-Based Indexing.},
author = {Tobin Jr, Kenneth William and Bhaduri, Budhendra L and Bright, Eddie A and Cheriydat, Anil and Karnowski, Thomas Paul and Palathingal, Paul J and Potok, Thomas E and Price, Jeffery R},
abstractNote = {We describe a method for indexing and retrieving high-resolution image regions in large geospatial data libraries. An automated feature extraction method is used that generates a unique and specific structural description of each segment of a tessellated input image file. These tessellated regions are then merged into similar groups, or sub-regions, and indexed to provide flexible and varied retrieval in a query-by-example environment. The methods of tessellation, feature extraction, sub-region clustering, indexing, and retrieval are described and demonstrated using a geospatial library representing a 153 km2 region of land in East Tennessee at 0.5 m per pixel resolution.},
doi = {10.14358/PERS.72.5.531},
journal = {Photogrammetric Engineering & Remote Sensing},
number = 5,
volume = 72,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2006},
month = {Mon May 01 00:00:00 EDT 2006}
}
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Journal Article:
DOI:  10.14358/PERS.72.5.531
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  • ; ; ; ...
    We describe a method for indexing and retrieving high-resolution image regions in large geospatial data libraries. An automated feature extraction method is used that generates a unique and specific structural description of each segment of a tessellated input image file. These tessellated regions are then merged into similar groups and indexed to provide flexible and varied retrieval in a query-by-example environment.

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