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The Advanced Detection Technology Sensor can detect, discriminate, and classify stationary ground targets-during the day or night-even through cloud cover, fog, smoke, dust, or rain. The sensor is a coherent, fully polarimetric, 35-GHz synthetic-aperture radar (SAR) with a resolution of 1 ft x 1 ft. And, to minimize SAR speckle while preserving image(More)
s Lincoln Laboratory is investigating the use of high-resolution fully polarimetric synthetic-aperture radar (SAR) imagery to detect and classify stationary ground targets. This article summarizes a study in which data collected by the Lincoln Laboratory 33-GHz SAR were used to perform a comprehensive comparison of automatic target recognition (ATR)(More)
s Lincoln Laboratory is investigating the detection, discrimination, and classification of ground targets in high-resolution, fully polarimetric, synthetic-aperture radar (SAR) imagery. This paper summarizes our work in SAR automatic target recognition by discussing the prescreening, discrimination, and classification algorithms we have developed; data from(More)
This paper presents a solution to the coherent change detection (CCD) problem using multi-polarization synthetic aperture radar (SAR) imagery. The multi-polarization SAR imagery (i.e., the day-1 reference and day-2 test images) are modeled as jointly correlated complex Gaussian vectors with unknown correlation, j e φ ρ γ =. Maximum likelihood estimates of(More)
The application of spatial matched filter classifiers to the synthetic aperture radar (SAR) automatic target recognition (ATR) problem is being investigated at MIT Lincoln Laboratory. Initial studies investigating the use of several different spatial matched filter classifiers in the framework of a 2D SAR ATR system are summarized. In particular, a new(More)
187 T    automatic target recognition (ATR) is to detect and recognize objects, such as tanks, in images produced by a laser radar, a synthetic-aperture radar (SAR), or an infrared or video camera. Current ATR methods are not fully automatic , relying instead on a partnership between computer processing and human analysis. Although computer(More)
This paper presents the development, analysis and validation of a new target discrimination module for synthetic aperture radar (SAR) imagery based on an extension of gamma functions to 2-D. Using the two parameter CFAR stencil as a prototype, a new stencil based on 2-D gamma functions is used to estimate the intensity of the pixel under test and its(More)