Authors: Rupert Paget, John Homer, and David Crisp (Automatic) Target Detection in Synthetic Aperture Radar Imagery Via Terrain Recognition Authors: Rupert Paget, John Homer, and David Crisp THE UNIVERSITY OF QUEENSLAND AUSTRALIA Cooperative Research Centre for Sensor Signal and Information Processing

Contents The problem Markov random field texture model Open ended texture classification Target detection The results Conclusion

The Problem To identify real targets from background texture. Surveillance of large areas of the earths surface is often undertaken with low resolution synthetic aperture radar (SAR) imagery from either a satellite or a plane. There is a need to process these images with automatic target detection (ATD) algorithms. Identified real targets False targets

The Problem Typically the targets being searched for are vehicles or small vessels, which occupy only a few resolution cells. Simple thresholding is usually inadequate for detection due to the high amount of noise in the images. Often the background has a discernible texture, and one form of detection is to search for anomalies in the texture caused by the presence of the target pixels. Identified real targets False targets

The Problem To perform this task a texture model must be able to model a variety of textures at run time, and also model these textures well enough to detect anomalies. We accomplish this with our multiscale nonparametric Markov random field (MRF) texture model. Identified real targets False targets

Markov Random Field Model Is formed by modelling the value of the centre pixel in terms of a conditional probability with respect to its neighbouring pixels values.

Nonparametric MRF Model Built from a multidimensional histogram. Does not require parameter estimation. Can model high dimensional statistics.

Strong Nonparametric MRF Where the multidimensional histogram is represented as a combination of marginal histograms. This allows control over the statistical order of the model.

Synthetic Textures Comparative analysis of the synthetic textures shows that the texture model can capture the unique characteristics of various textures.

Open Ended Classification To perform target detection, or anomaly detection, we will use our open ended texture classifier. It is based on the notion that if a texture model is able to capture the unique characteristics of a texture, then the distribution of those characteristics or features define the texture. Conventional N class classifier Open ended classifier

Open Ended Classification A texture is classified if it has the same set of characteristics or features as a predefined texture. This is resolved via a goodness-of-fit test between the two sets of characteristics. Such a method allows the unknown or uncommitted subspace to be left undefined. Conventional N class classifier Open ended classifier

Goodness-of-fit Test Require a population of measurements. Most reliable results are from one-dimensional statistics. Therefore: We use the nonparametric model to obtain histograms, using the data points as features or measurements. This gives us a “population” of measurements. To obtain one-dimensional statistics from a multi-dimensional histogram, we discard the positional information and just use the frequencies or probabilities or distance to the nearest neighbour associated with the data points.

Target Detection Given that the images have been pre-segmented, we wish to determine whether there is a target in the centre of some undefined texture. First, build the histograms for the nonparametric MRF model of the background texture. For each histogram, create a set of one dimensional statistics for both background texture and target. These sets of one dimensional statistics can again be reduced to just one set of one dimensional statistics. Perform a goodness-of-fit on this set of statistics. We used the nonparametric Kruskal-Wallis test.

Results MRF Model %True Targets % False Targets Difference n1c0t0w2 88.5167 12.5846 75.9321 n1c0t0w4 94.0191 12.5056 81.5135 n1c0t0w6 93.5407 11.7728 81.7679 n1c0t1w2 60.5263 33.1926 27.3337 n1c0t1w4 82.2967 39.2159 43.0808 n1c0t1w6 86.6029 38.6314 47.9715 n1c2t0w2 16.7668 76.7739 n1c2t0w4 97.6077 24.9306 72.6771 n1c2t0w6 95.6938 21.5264 74.1674 n1c2t1w2 31.1005 22.0496 9.05090 n1c2t1w4 87.0813 43.5676 43.5137 n1c2t1w6 84.2105 29.9600 54.2505 Nearest neighbour neighbourhood nonparametric MRF models with their best target discrimination performance.

Results MRF Model %True Targets % False Targets Difference n3c0t0w2 84.6890 10.6531 74.0359 n3c0t0w4 96.6507 18.9497 77.7010 n3c0t0w6 93.7799 14.7908 78.9891 n3c0t1w2 54.0670 27.4947 26.5723 n3c0t1w4 83.7321 38.4853 45.2468 n3c0t1w6 84.4498 33.2018 51.2480 n3c2t0w2 95.6938 26.4195 69.2743 n3c2t0w4 99.7608 46.1267 53.6341 n3c2t0w6 97.8469 35.9212 61.9257 n3c2t1w2 60.7656 40.0080 20.7576 n3c2t1w4 80.1435 23.8357 56.3078 n3c2t1w6 85.4067 24.3666 61.0401 3x3 neighbourhood nonparametric MRF models with their best target discrimination performance.

Results MRF Model %True Targets % False Targets Difference n0t0w2 79.6651 13.0061 66.6590 n0t0w4 87.7990 14.1505 73.6485 n0t0w6 84.4498 9.27080 75.1790 n0t1w2 46.4115 30.5805 15.8310 n0t1w4 51.1962 21.4410 29.7552 n0t1w6 83.7321 33.3387 50.3934 Histograms %True Targets % False Targets Difference t0w2 80.6220 16.7287 63.8933 t0w4 94.0191 40.9498 53.0693 t0w6 86.1244 37.3967 48.7277 t1w2 99.0431 54.9217 44.1214 t1w4 98.0861 51.3008 46.7853 t1w6 84.9282 37.8322 47.0960 Control models with their best target discrimination performance.

Conclusion The results were obtained from a DSTO data set containing 142067 pre-segmentated images of possible targets. 418 of these images were ground truthed as having real targets. Our best results were able to reduce the number of false targets to 11.8% while retaining 93.5% of the true targets. This texture discrimination method was shown to be better than comparable grey level discrimination.

Conclusion Future direction of this research is to increase the speed of the algorithm. This may require new discriminating features. This will allow implementation of the algorithm on a larger DSTO target detection database. From these future results we will be able to compare our method with current target detection methods.