1. Multi-Classifier Buried Mine Detection Using MWIR Images Dr. Bo Ling Migma Systems, Inc. Mr. Anh H. Trang Mr. Chung Phan US Army RDECOM April 10, 2007 Defense and Security Symposium 2007

2. Presentation Outline - Overall Technical Approach - MWIR Image Thresholding and Clustering - Buried Mine Directional Signatures - Multi-Classifier for Buried Mine Detection - Test Results - Conclusion

3. Overall Approach Image Thresholding Image Clustering Image Windowing Buried Mine Classifier (horizontal) Buried Mine Image Buried Mine Classifier (vertical) Buried Mine Classifier (diagonal) Fusion

4. Image Thresholding Using Wavelet Transform Image Thresholding Based on Inverse Wavelet Transform where is related to the inverse of discrete wavelet transform, t h, t v, and t d are the threshold values associated with three decompositions in the wavelet domain.

5. Image Thresholding Original ImageThresholded Image Thresholding method has preserved the surface and buried mines.

6. Image Clustering Image Thresholding Image Clustering Image Windowing Buried Mine Classifier (horizontal) Buried Mine Image Buried Mine Classifier (vertical) Buried Mine Classifier (diagonal) Fusion

7. Adaptive Self-Organizing Maps (ASOM) Data No prior knowledge of number of clusters neurons Neuron activation function Similarity Measurement

8. Clustering after Thresholding Clustering ClustersThresholded Image Each cluster represents a potential mine

9. Buried Mine Signatures The similarity-based 3D ASOM is used to find clusters in the windowed target chip. Original Image Target Chip Clusters

10. Directional Scanning We build buried mine signatures in three directions Horizontal Scan Vertical ScanDiagonal Scan

11. Library of Buried Mine Signatures We have found that the thermal variation patterns exhibited in daytime and nighttime are significantly different.

12. Signature Vectors Horizontal Signatures Vertical SignatureDiagonal Signature

13. Example of Buried Mine Signatures Target Chip Signature Histogram The signatures associated with buried mines are common in - Long vector length - Histogram peaked in the middle

14. Signature Comparison Mine Signatures

15. False Alarm Mitigation Signature difference can be used to eliminate false alarms.

16. Multi-Classifier Detection Image Thresholding Image Clustering Image Windowing Buried Mine Classifier (horizontal) Buried Mine Image Buried Mine Classifier (vertical) Buried Mine Classifier (diagonal) Fusion

17. Three Directional Classifiers Horizontal Classifier Vertical Classifier Diagonal Classifier Each of three classifiers will process the corresponding directional signatures.

18. Test Result of Nighttime Image We have tested both daytime and nighttime images taken from MWIR data collected as part of Lightweight Airborne Multispectral Minefield Detection (LAMD) program. Original Image

19. Test Result - Clustering Original ImageClustered Image Since each cluster could represent a buried mine, we must process all clusters.

20. Test Result - Three Classifiers Each cluster is windowed and processed by all three directional classifiers. There are three independent detection results. Three false alarms Four false alarms

21. Test Result - Fusion We have used a simple fusion scheme: a buried mine is declared only if it is detected by all three classifiers. One advantage of this type of fusion is low false alarm rate since three classifiers may not report the same false detection in the same image.

22. Final Detection Two false alarms left. They can be further eliminated.

23. Conclusion For each target chip, we scan it in three directions: vertical, horizontal, and diagonal to construct three signatures. For the same target chip, there will be a total of three classifiers associated with vertical, horizontal, and diagonal scans. These three classifiers are applied to the same target chip, resulting in three independent detection results, which are further fused for a refined detection. New results will be reported in the future once we test the system with new images.