(Automatic) Target Detection in Synthetic Aperture Radar Imagery Via Terrain Recognition

1. (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

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

3. 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

4. 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

5. 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

6. 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.

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

8. 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.

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

10. 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

11. 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

12. 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.

13. 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.

14. Results • Nearest neighbour neighbourhood nonparametric MRF models with their best target discrimination performance.

15. Results • 3x3 neighbourhood nonparametric MRF models with their best target discrimination performance.

16. Results • Control models with their best target discrimination performance.

17. 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.

18. 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.