Multi-Classifier Buried Mine Detection Using MWIR Images Dr. Bo Ling Migma Systems, Inc. Mr. Anh H....
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Multi-Classifier Buried Mine Detection Using MWIR Images
Dr. Bo LingMigma Systems, Inc.
Mr. Anh H. Trang Mr. Chung PhanUS Army RDECOM
April 10, 2007
Defense and Security Symposium 2007
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Presentation Outline
- Overall Technical Approach
- MWIR Image Thresholding and Clustering
- Buried Mine Directional Signatures
- Multi-Classifier for Buried Mine Detection
- Test Results
- Conclusion
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Overall Approach
Image Thresholding
Image Clustering
Image Windowing
Buried Mine Classifier
(horizontal)
Buried MineImage
Buried Mine Classifier(vertical)
Buried Mine Classifier(diagonal)
Fusion
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Image Thresholding Using Wavelet Transform
Image Thresholding Based on Inverse Wavelet Transform
imagei
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YifYY
ˆ
dvhWimage tttf ,,ˆ1
where is related to the inverse of discrete wavelet transform, th, tv , and td are the threshold values associated with three decompositions in the wavelet domain.
1Wf
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Image Thresholding
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Original Image Thresholded Image
Thresholding method has preserved the surface and buried mines.
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Image Clustering
Image Thresholding
Image Clustering
Image Windowing
Buried Mine Classifier
(horizontal)
Buried MineImage
Buried Mine Classifier(vertical)
Buried Mine Classifier(diagonal)
Fusion
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Adaptive Self-Organizing Maps (ASOM)
Data
No prior knowledge of number of clusters
neurons
Neuron activation function
Similarity Measurement
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Clustering after Thresholding
Clustering
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ClustersThresholded Image
Each cluster represents a potential mine
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Buried Mine Signatures
The similarity-based 3D ASOM is used to find clusters in the windowed target chip.
Original ImageTarget Chip Clusters
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Directional Scanning
We build buried mine signatures in three directions
Horizontal Scan Vertical Scan Diagonal Scan
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Library of Buried Mine Signatures
We have found that the thermal variation patterns exhibited in daytime and nighttime are significantly different.
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Signature Vectors
Horizontal Signatures Vertical Signature Diagonal Signature
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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
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Signature Comparison
Mine Signatures
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False Alarm Mitigation
Signature difference can be used to eliminate false alarms.
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Multi-Classifier Detection
Image Thresholding
Image Clustering
Image Windowing
Buried Mine Classifier
(horizontal)
Buried MineImage
Buried Mine Classifier(vertical)
Buried Mine Classifier(diagonal)
Fusion
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Three Directional Classifiers
Horizontal Classifier
Vertical Classifier
Diagonal Classifier Each of three
classifiers will process the corresponding directional signatures.
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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
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Test Result - Clustering
Original Image Clustered Image
Since each cluster could represent a buried mine, we must process all clusters.
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Test Result - Three Classifiers
Each cluster is windowed and processed by all three directional classifiers.
There are three independent detection results.
Three false alarms Three false alarms Four false alarms
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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.
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Final Detection
Two false alarms left.
They can be further eliminated.
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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.