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Title: Building detection in SAR imagery.

Abstract

Abstract not provided.

Authors:
; ; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1248844
Report Number(s):
SAND2015-2974C
583327
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the SPIE Defense + Security Radar Sensor Technology 2015 held April 20-24, 2015 in Baltimore, Maryland.
Country of Publication:
United States
Language:
English

Citation Formats

Steinbach, Ryan Matthew, Koch, Mark William, Moya, Mary M, and Goold, Jeremy. Building detection in SAR imagery.. United States: N. p., 2015. Web.
Steinbach, Ryan Matthew, Koch, Mark William, Moya, Mary M, & Goold, Jeremy. Building detection in SAR imagery.. United States.
Steinbach, Ryan Matthew, Koch, Mark William, Moya, Mary M, and Goold, Jeremy. Wed . "Building detection in SAR imagery.". United States. doi:. https://www.osti.gov/servlets/purl/1248844.
@article{osti_1248844,
title = {Building detection in SAR imagery.},
author = {Steinbach, Ryan Matthew and Koch, Mark William and Moya, Mary M and Goold, Jeremy},
abstractNote = {Abstract not provided.},
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}
}

Conference:
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  • 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.more » 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.« 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
  • The adaptive matched filter was implemented as a spatial detector for amplitude-only or complex images, and applied to an image formed by standard narrow band means from a wide angle, wideband radar. Direct performance comparisons were made between different implementations and various matched and mismatched cases by using a novel approach to generate ROC curves parametrically. For perfectly matched cases, performance using imaged targets was found to be significantly lower than potential performance of artificial targets whose features differed from the background. Incremental gain due to whitening the background was also found to be small, indicating little background spatial correlation.more » It is conjectured that the relatively featureless behavior in both targets and background is due to the image formation process, since this technique averages together all wide angle, wideband information. For mismatched cases where the signature was unknown, the amplitude detector losses were approximately equal to whatever gain over noncoherent integration that matching provided. However, the complex detector was generally very sensitive to unknown information, especially phase, and produced much larger losses. Whitening under these mismatched conditions produced further losses. Detector choice thus depends primarily on how reproducible target signatures are, especially if phase is used, and the subsequent number of stored signatures necessary to account for various imaging aspect angles.« less
  • In this paper we investigate the applicability of the feature extraction mechanisms found in the neurophysiology of mammals to the problem of object recognition in synthetic aperture radar imagery. Our approach is to present multiple views of objects to be recognized to a two-stage self-organizing neural network architecture. The first stage, a two-layer Neocognitron, performs feature extraction in each layer The resulting feature vectors are presented to the second stage, an ART-2A classifier self-organizing neural network which clusters the features into multiple object categories. The feature extraction operators resulting from the self-organization process are compared to the feature extraction mechanismsmore » found in the neurophysiology of vision. In a previous paper, the Neocognitron was trained on raw SAR imagery. The architecture was able to recognize a simulated vehicle at arbitrary azimuthal orientations at a single depression angle while rejecting clutter as well as other vehicles. Feature extraction on raw imagery yielded features that were robust but very difficult to interpret. In this paper we report the results of some new experiments in which the self-organization process is applied separately to shadow and bright returns from objects to be recognized. Feature extraction on shadow returns yield oriented contrast edge operators suggestive of bipartite simple cells observed in the striate cortex of mammals. Feature extraction on the specularity patterns in bright returns yield a collection of operators resembling a twodimensional Haar basis set. We compare the performance of the earlier two-stage neural network trained on raw imagery with a modified network using the new feature set.« less
  • While many synthetic aperture radar (SAR) applications use only detected imagery, dramatic improvements in resolution and employment of algorithms requiring complex-valued SAR imagery suggest the need for compression of complex data. Here, we investigate the benefits of using complex- valued wavelets on complex SAR imagery in the embedded zerotree wavelet compression algorithm, compared to using real-valued wavelets applied separately to the real and imaginary components. This compression is applied at low ratios (4:1-12:1) for high fidelity output. The complex spatial correlation metric is used to numerically evaluate quality. Numerical results are tabulated and original and decompressed imagery are presented asmore » well as correlation maps to allow visual comparisons.« less