Performance of a high-resolution polarimetric SAR automatic target recognition system

s Lincoln Laboratory is investigating the detection, discrimination, and classification of ground targets in high-resolution, fully polarimetric, syntheticaperture 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 5 km 2 of clutter and 339 targets were used to study the performance of these algorithms. The prescreener required a low threshold to detect most of the targets in the data, which resulted in a high density of false alarms. The discriminator and classifier stages then reduced this false-alarm density by a factor of 100. We improved targetdetection performance by using fully polarimetric imagery processed by the polarimetric whitening filter (PWF), rather than by using single-channel imagery. In addition, the PWF-processed imagery improved the probability of correct classification in a four-class (tank, armored personnel carrier, howitzer, or clutter) classifier. T - program is a broad-based advanced technology program to develop new weapons technology that can locate and destroy critical mobile targets such as SCUD launch systems and other highly mobile platforms. Automatic target recognition (ATR) is an important candidate technology for this effort. To address the surveillance and targeting aspects of the Warbreaker program, Lincoln Laboratory has developed a complete, end-to-end, 2-D synthetic-aperture radar (SAR) ATR system. This system requires a sensor that can search large areas and also provide fine enough resolution to detect and identify mobile targets in a variety of landscapes and deployments. The Lincoln Laboratory ATR system has three basic stages: detection (or prescreening), discrimination, and classification (see To evaluate the performance of the ATR system

Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery

The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition