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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)
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)
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)
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)
This paper evaluates the performance of the recently published wavelet based algorithm for speckle reduction of SAR images. The original algorithm, based on the theory of wavelet thresholding due to Donoho and Johnstone, has been shown to improve speckle statistics. In this paper we give more extensive results based on tests performed at Lincoln Laboratory(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)