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Title: Automatic Change Detection in Synthetic Aperture Radar (SAR)


No abstract provided.

 [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Technical Report
Country of Publication:
United States

Citation Formats

Murphy, David Patrick, and Calef, Matthew T. Automatic Change Detection in Synthetic Aperture Radar (SAR). United States: N. p., 2017. Web. doi:10.2172/1375848.
Murphy, David Patrick, & Calef, Matthew T. Automatic Change Detection in Synthetic Aperture Radar (SAR). United States. doi:10.2172/1375848.
Murphy, David Patrick, and Calef, Matthew T. 2017. "Automatic Change Detection in Synthetic Aperture Radar (SAR)". United States. doi:10.2172/1375848.
title = {Automatic Change Detection in Synthetic Aperture Radar (SAR)},
author = {Murphy, David Patrick and Calef, Matthew T.},
abstractNote = {No abstract provided.},
doi = {10.2172/1375848},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2017,
month = 8

Technical Report:

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  • The intent of this study is to provide an analysis of the scattering from a crevasse in Antarctic ice, utilizing a physics-based model for the scattering process. Of primary interest is a crevasse covered with a snow bridge, which makes the crevasse undetectable in visible-light images. It is demonstrated that a crevasse covered with a snow bridge can be visible in synthetic-aperture-radar (SAR) images. The model of the crevasse and snow bridge incorporates a complex dielectric permittivity model for dry snow and ice that takes into account the density profile of the glacier. The surface structure is based on amore » fractal model that can produce sastrugi-like features found on the surface of Antarctic glaciers. Simulated phase histories, computed with the Shooting and Bouncing Ray (SBR) method, are processed into SAR images. The viability of the SBR method for predicting scattering from a crevasse covered with a snow bridge is demonstrated. Some suggestions for improving the model are given.« less
  • Radar non-acoustic anti-submarine warfare (NAASW) became the subject of considerable scientific investigation and controversy in the West subsequent to the discovery by the Seasat satellite in 1978 that manifestations of underwater topography, thought to be hidden from the radar, were visible in synthetic aperture radar (SAR) images of the ocean. In addition, the Seasat radar produced images of ship wakes where the observed angle between the wake arms was much smaller than expected from classical Kelvin wake theory. These observations cast doubt on the radar oceanography community's ability to adequately explain these phenomena, and by extension on the ability ofmore » existing hydrodynamic and radar scattering models to accurately predict the observability of submarine-induced signatures. If one is of the opinion that radar NAASW is indeed a potentially significant tool in detecting submerged operational submarines, then the Soviet capability, as evidenced throughout this report, will be somewhat daunting. It will be shown that the Soviets have extremely fine capabilities in both theoretical and experimental hydrodynamics, that Soviet researchers have been conducting at-sea radar remote sensing experiments on a scale comparable to those of the United States for several years longer than we have, and that they have both an airborne and spaceborne SAR capability. The only discipline that the Soviet Union appears to be lacking is in the area of digital radar signal processing. If one is of the opinion that radar NAASW can have at most a minimal impact on the detection of submerged submarines, then the Soviet effort is of little consequence and poses not threat. 280 refs., 31 figs., 12 tabs.« less
  • This report provides a detailed evaluation of synthetic aperture radar (SAR) as a potential technology improvement over the Coast Guard's existing side-looking airborne radar (SLAR) for oil-spill surveillance applications. The U.S. Coast Guard Research and Development Center (RD Center), Environmental Safety Branch, sponsored a joint experiment including the U.S. Coast Guard, Sandia National Laboratories, and the National Oceanographic and Atmospheric Administration (NOAA), Hazardous Materials Division. Radar imaging missions were flown on six days over the coastal waters off Santa Barbara, CA, where there are constant natural seeps of oil. Both the Coast Guard SLAR and the Sandia National Laboratories SARmore » were employed to acquire simultaneous images of oil slicks and other natural sea surface features that impact oil-spill interpretation. Surface truth and other environmental data were also recorded during the experiment. The experiment data were processed at Sandia National Laboratories and delivered to the RD Center on a PC-based computer workstation for analysis by experiment participants. Synthetic aperture radar, Side looking airborne radar, Oil slicks.« less
  • Synthetic Aperture Radar (SAR) imaging is a class of coherent range and Doppler signal processing techniques applied to remote sensing. The aperture is synthesized by recording and processing coherent signals at known positions along the flight path. Demands for greater image resolution put an extreme burden on requirements for inertial measurement units that are used to maintain accurate pulse-to-pulse position information. The recently developed Phase Gradient Autofocus algorithm relieves this burden by taking a data-driven digital signal processing approach to estimating the range-invariant phase aberrations due to either uncompensated motions of the SAR platform or to atmospheric turbulence. Although themore » performance of this four-step algorithm has been demonstrated, its convergence has not been modeled mathematically. A new sensitivity study of algorithm performance is a necessary step towards this model. Insights that are significant to the application of this algorithm to both SAR and to other coherent imaging applications are developed. New details on algorithm implementation identify an easily avoided biased phase estimate. A new algorithm for defining support of the point spread function is proposed, which promises to reduce the number of iterations required even for rural scenes with low signal-to-clutter ratios.« less
  • This report summarizes the work performed for the Office of the Chief of Naval Research (ONR) during the period of 1 September 1997 through 31 December 1997. The primary objective of this research was aimed at developing an alternative time-frequency approach which is recursive-in-time to be applied to the Inverse Synthethic Aperture Radar (ISAR) imaging problem discussed subsequently. Our short term (Phase I) goals were to: 1. Develop an ISAR stepped-frequency waveform (SFWF) radar simulator based on a point scatterer vehicular target model incorporating both translational and rotational motion; 2. Develop a parametric, recursive-in-time approach to the ISAR target imagingmore » problem; 3. Apply the standard time-frequency short-term Fourier transform (STFT) estimator, initially to a synthesized data set; and 4. Initiate the development of the recursive algorithm. We have achieved all of these goals during the Phase I of the project and plan to complete the overall development, application and comparison of the parametric approach to other time-frequency estimators (STFT, etc.) on our synthesized vehicular data sets during the next phase of funding. It should also be noted that we developed a batch minimum variance translational motion compensation (TMC) algorithm to estimate the radial components of target motion (see Section IV). This algorithm is easily extended to recursive solution and will probably become part of the overall recursive processing approach to solve the ISAR imaging problem. Our goals for the continued effort are to: 1. Develop and extend a complex, recursive-in-time, time- frequency parameter estimator based on the recursive prediction error method (RPEM) using the underlying Gauss- Newton algorithms. 2. Apply the complex RPEM algorithm to synthesized ISAR data using the above simulator. 3. Compare the performance of the proposed algorithm to standard time-frequency estimators applied to the same data sets.« less