1. 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

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
MRF Model
%True Targets
% False Targets
Difference
n1c0t0w2
n1c0t0w4
n1c0t0w6
n1c0t1w2
n1c0t1w4
n1c0t1w6
n1c2t0w2
n1c2t0w4
n1c2t0w6
n1c2t1w2
n1c2t1w4
n1c2t1w6
Nearest neighbour neighbourhood nonparametric MRF models with their best target discrimination performance.

15. Results
MRF Model
%True Targets
% False Targets
Difference
n3c0t0w2
n3c0t0w4
n3c0t0w6
n3c0t1w2
n3c0t1w4
n3c0t1w6
n3c2t0w2
n3c2t0w4
n3c2t0w6
n3c2t1w2
n3c2t1w4
n3c2t1w6
3x3 neighbourhood nonparametric MRF models with their best target discrimination performance.

16. Results
MRF Model
%True Targets
% False Targets
Difference
n0t0w2
n0t0w4
n0t0w6
n0t1w2
n0t1w4
n0t1w6
Histograms
%True Targets
% False Targets
Difference
t0w2
t0w4
t0w6
t1w2
t1w4
t1w6
Control models with their best target discrimination performance.

17. Conclusion
The results were obtained from a DSTO data set containing 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.