Building Detection in SAR Imagery
Abstract
Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. The desire is to present a technique that is effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed technique assumes that for the desired image, bright lines and shadows, SAR artifact effects, are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint. Where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. Constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results aremore »
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1171460
- Report Number(s):
- SAND2014-16584R
534500
- DOE Contract Number:
- AC04-94AL85000
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION; SAR; Building Detection; SAR artifact effects; shadows; bright lines
Citation Formats
Steinbach, Ryan Matthew, Koch, Mark William, Moya, Mary M, and Goold, Jeremy. Building Detection in SAR Imagery. United States: N. p., 2014.
Web. doi:10.2172/1171460.
Steinbach, Ryan Matthew, Koch, Mark William, Moya, Mary M, & Goold, Jeremy. Building Detection in SAR Imagery. United States. doi:10.2172/1171460.
Steinbach, Ryan Matthew, Koch, Mark William, Moya, Mary M, and Goold, Jeremy. Fri .
"Building Detection in SAR Imagery". United States.
doi:10.2172/1171460. https://www.osti.gov/servlets/purl/1171460.
@article{osti_1171460,
title = {Building Detection in SAR Imagery},
author = {Steinbach, Ryan Matthew and Koch, Mark William and Moya, Mary M and Goold, Jeremy},
abstractNote = {Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. The desire is to present a technique that is effective and efficient at determining an approximate building location. This approximate location can be used to extract a portion of the SAR image to then perform a more robust detection. The proposed technique assumes that for the desired image, bright lines and shadows, SAR artifact effects, are approximately labeled. These labels are enhanced and utilized to locate buildings, only if the related bright lines and shadows can be grouped. In order to find which of the bright lines and shadows are related, all of the bright lines are connected to all of the shadows. This allows the problem to be solved from a connected graph viewpoint. Where the nodes are the bright lines and shadows and the arcs are the connections between bright lines and shadows. Constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results are provided showing the outcome of the technique.},
doi = {10.2172/1171460},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Aug 01 00:00:00 EDT 2014},
month = {Fri Aug 01 00:00:00 EDT 2014}
}