skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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. 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 » provided showing the outcome of the technique.« less

Authors:
 [1];  [1];  [1];  [1]
  1. 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}
}

Technical Report:

Save / Share:
  • Median filtering reduces speckle in synthetic aperture radar (SAR) imagery while preserving edges, at the expense of coarsening the resolution, by replacing the center pixel of a sliding window by the median value. For shadow detection, this approach helps distinguish shadows from clutter more easily, while preserving shadow shape delineations. However, the nonlinear operation alters the shadow and clutter distributions and statistics, which must be taken into consideration when computing probability of detection and false alarm metrics. Depending on system parameters, median filtering can improve probability of detection and false alarm by orders of magnitude. Herein, we examine shadow probabilitymore » of detection and false alarm in a homogeneous, ideal clutter background after median filter post-processing. Some comments on multi-look processing effects with and without median filtering are also made.« less
  • 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 tomore » 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.« less
  • Abstract not provided.
  • Wavefront curvature defocus effects occur in spotlight-mode SAR imagery when reconstructed via the well-known polar-formatting algorithm (PFA) under certain imaging scenarios. These include imaging at close range, using a very low radar center frequency, utilizing high resolution, and/or imaging very large scenes. Wavefront curvature effects arise from the unrealistic assumption of strictly planar wavefronts illuminating the imaged scene. This dissertation presents a method for the correction of wavefront curvature defocus effects under these scenarios, concentrating on the generalized: squint-mode imaging scenario and its computational aspects. This correction is accomplished through an efficient one-dimensional, image domain filter applied as a post-processingmore » step to PF.4. This post-filter, referred to as SVPF, is precalculated from a theoretical derivation of the wavefront curvature effect and varies as a function of scene location. Prior to SVPF, severe restrictions were placed on the imaged scene size in order to avoid defocus effects under these scenarios when using PFA. The SVPF algorithm eliminates the need for scene size restrictions when wavefront curvature effects are present, correcting for wavefront curvature in broadside as well as squinted collection modes while imposing little additional computational penalty for squinted images. This dissertation covers the theoretical development, implementation and analysis of the generalized, squint-mode SVPF algorithm (of which broadside-mode is a special case) and provides examples of its capabilities and limitations as well as offering guidelines for maximizing its computational efficiency. Tradeoffs between the PFA/SVPF combination and other spotlight-mode SAR image formation techniques are discussed with regard to computational burden, image quality, and imaging geometry constraints. It is demonstrated that other methods fail to exhibit a clear computational advantage over polar-formatting in conjunction with SVPF. This research concludes that PFA in conjunction with SVPF provides a computationally efficient spotlight-mode image formation solution that solves the wavefront curvature problem for most standoff distances and patch sizes, regardless of squint, resolution or radar center frequency. Additional advantages are that SVPF is not iterative and has no dependence on the visual contents of the scene: resulting in a deterministic computational complexity which typically adds only thirty percent to the overall image formation time.« less
  • While typical SAR imaging employs a co-located (monostatic) RADAR transmitter and receiver, bistatic SAR imaging separates the transmitter and receiver locations. The transmitter and receiver geometry determines if the scattered signal is back scatter, forward scatter, or side scatter. The monostatic SAR image is backscatter. Therefore, depending on the transmitter/receiver collection geometry, the captured imagery may be quite different that that sensed at the monostatic SAR. This document presents imagery and image products formed from captured signals during the validation stage of the bistatic SAR research. Image quality and image characteristics are discussed first. Then image products such as two-colormore » multi-view (2CMV) and coherent change detection (CCD) are presented.« less