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Our approach involves a careful integration of sensor physics, statistical analysis and modeling, and IU methods. In particular, we plan to take the following approach to each of the interrelated topics.

Multiresolution SAR-based ATR

One of our major successes to date under the current ATR-URI project is in developing multiresolution stochastic models for SAR imagery that accurately capture the scale-to-scale statistical variability of speckle in SAR imagery. In one application, we used models for natural clutter and for man-made objects together with our fast statistical likelihood calculation methods to develop an enhanced discrimination feature that, when integrated into Lincoln Labs' ATR algorithm and tested on a very large data set, resulted in a factor of 6 reduction in false alarms over the previous best results. In the second application, we used our models to segment natural clutter (trees and grass) and to enhance anomalous pixels (due to man-made scatterers) that did not produce the scale-to-scale variability consistent with natural clutter. The results are very accurate segmentations and enhanced visibility of anomalies as compared to widely used CFAR methods.

We believe there are many additional applications for these multiscale models. For example, anomalies that result from man-made objects exhibit themselves as distinctive patterns across scale that differ significantly from the scale-to-scale textural variations. Consequently, chains of pixels across scale could in principle be viewed as robust and statistically meaningful features that can be further exploited for model-based recognition. We propose to use multiscale features for higher level recognition and reasoning. Classically, target models include geometrical constraints on the appearance of features in space. In this new framework, models will also include information about the appearance of features across scale. The development of such models is a central objective of this project. Once we have such models, we can use our statistically optimal methods for evaluating likelihoods to evaluate match scores for hypothesized models and poses.

SAR-based ATR incorporating pose-dependent SAR image formation and analysis.

A widely recognized fact in the ATR community is that the scattering patterns for man-made scatterers possess very different characteristics from those of natural scatterers. In particular, while the latter frequently can be modeled as diffuse isotropic scatterers, the former frequently have strong specular characteristics, which implies that they have extremely strong aspect-dependent responses. This effect is even more pronounced in low-frequency SAR as is used for foliage penetration.

While some work has attempted to account for the difference in scatterer type, it is fair to say that a fundamental look at this problem has yet to be taken. In some very recent research Prof. Shapiro has begun to do this. His analysis indicates that there is significant discriminating information to be obtained if one examines sets of SAR images of a scene constructed using different subapertures of the full SAR aperture. What this suggests is that the natural data structure for higher-level ATR functions will again involve imagery at different resolutions, but that in this case the focus is on exploiting differences in imagery obtained from different parts of the aperture (and hence from different viewing angles). Note that this results in a very novel pose estimation problem: the model of a specular reflector must capture the fact that changes in pose not only change the relative geometry of features but also can change the appearance of these features in imagery from different apertures. As a result, the action of the pose transformation group on a set of images from different apertures and at different resolutions is not simply a geometric transformation.

Organization: Massachusetts Institute of Technology
Department: Artificial Intelligence Lab
Principle Investigators: Paul Viola
Eric Grimson
Alan Willsky
Other Investigators: John Fisher
Jeremy S. De Bonet
Jeffrey Shapiro
William Wells
Technical Area: Automatic Target Recognition (ATR) at the MIT AI Lab

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Eric Grimson ( welg@ai.mit.edu)
Paul Viola ( viola@ai.mit.edu)
AI Lab Webmaster ( webmaster@ai.mit.edu)
Jeremy S. De Bonet ( jsd@ai.mit.edu)
Artificial Intelligence Laboratory
Massachusetts Institute of Technology
545 Technology Square (MIT NE43)
Cambridge, Massachusetts 02139

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