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Title: 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. I present two techniques that are 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 techniques assume 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. For the first technique, constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. The second technique calculates weights for the connections and then performs a series of increasingly relaxed hard and soft thresholds. Thismore » results in groups of various levels on their validity. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results demonstrate the outcome of the two techniques. The two techniques are compared and discussed.« less

Authors:
 [1]
  1. Univ. of Illinois, Urbana, IL (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:
1178360
Report Number(s):
SAND2015-2886T
583195
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Thesis/Dissertation
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Steinbach, Ryan Matthew. Building detection in SAR imagery. United States: N. p., 2015. Web.
Steinbach, Ryan Matthew. Building detection in SAR imagery. United States.
Steinbach, Ryan Matthew. Wed . "Building detection in SAR imagery". United States. doi:.
@article{osti_1178360,
title = {Building detection in SAR imagery},
author = {Steinbach, Ryan Matthew},
abstractNote = {Current techniques for building detection in Synthetic Aperture Radar (SAR) imagery can be computationally expensive and/or enforce stringent requirements for data acquisition. I present two techniques that are 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 techniques assume 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. For the first technique, constraints based on angle of depression and the relationship between connected bright lines and shadows are applied to remove unrelated arcs. The second technique calculates weights for the connections and then performs a series of increasingly relaxed hard and soft thresholds. This results in groups of various levels on their validity. Once the related bright lines and shadows are grouped, their locations are combined to provide an approximate building location. Experimental results demonstrate the outcome of the two techniques. The two techniques are compared and discussed.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Apr 01 00:00:00 EDT 2015},
month = {Wed Apr 01 00:00:00 EDT 2015}
}

Thesis/Dissertation:
Other availability
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