1. Bag-of-Visual-Words Based Feature Extraction
2017 The 9th International Conference on Digital Image Processing (ICDIP 2017)
Bag-of-Visual-Words Based Feature Extraction
for SAR Target Classification
[ID: A161]
Amrani Moussa
School of Computer Science and Technology,
Harbin Institute of Technology, China.

2. TABLE OF CONTENTS
INTRODUCTION
MOTIVATION
THE PROPOSED METHOD
EXPERIMENTAL RESULTS
CONCLUSIONS

3. INTRODUCTION Define Synthetic Aperture Radar (SAR).
Modes of SAR that are used.

4. INTRODUCTION
Synthetic Aperture Radar is a high-resolution airborne and spaceborne remote sensing system for imaging remote targets on a terrain.
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5. INTRODUCTION Decoys Target Optical Image High Resolution SAR
SAR collects data from multiple observation points and combines the received information coherently to achieve a very high-resolution description of the target.
Decoys
Target
High Resolution SAR
Optical Image
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6. INTRODUCTION Modes of SAR San SAR Spotlight SAR Side-looking SAR
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7. INTRODUCTION Modes of SAR San SAR Spotlight SAR Side-looking SAR
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8. MOTIVATION
Why SAR images?
Define the problems.

9. MOTIVATION Military surveillance Why SAR images ?
SAR has been primarily utilized for many applications on a target such as:
Military surveillance
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10. MOTIVATION Reconnaissance Why SAR images ?
SAR has been primarily utilized for many applications on a target such as:
Reconnaissance
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11. MOTIVATION Classification Why SAR images ?
SAR has been primarily utilized for many applications on a target such as:
Classification
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12. MOTIVATION Military surveillance Reconnaissance Classification
Why SAR images ?
SAR has been primarily utilized for many applications on a target such as:
Military surveillance
Reconnaissance
Classification
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13. MOTIVATION Define the problems
The understanding of SAR images is hard to carry on manual interpretation compared to optical images which describe a good appearance of a target.
Solutions
This suggests the development of automatic target recognition (ATR) algorithms for SAR images.
Developing a well-designed feature extraction method to achieve the desired results (i.e., accuracy and time).
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14. The proposed framework consists of the following three main steps:
THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
The proposed framework consists of the following three main steps:
Gabor feature extraction.
Using the BoVW paradigm to compute the bag of features based on k-means.
Using Support Vector Machine (SVM) as a baseline classifier for SAR target classification.
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15. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
Framework of the proposed method
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16. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
Feature extraction
1- Gabor filter design
. Gabor filters are invariance to rotation, scale, and translation , moreover it is variant with illumination changes and image noise .
. In this paper, sixty four Gabor filters are designed in the sense of the effectiveness of feature extraction. Therefore, Gabor filters with eight scales and eight orientations
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17. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
Feature extraction
2- Gabor feature extraction
- Gabor features are directly extracted from the gray-level SAR images, which are obtained by convolving the sample images with Gabor filters. Let I(x, y) be a sample SAR image, the Gabor representation of I(x, y) is denoted as:
- The size of the SAR images used in our experiments is 128*128 pixels. Using sixty four Gabor filters, the dimension of the feature vector is 128*128*64= Considering the adjacent pixels in our SAR images are highly correlated, the feature vectors resulting from Gabor filters are subsampled by a factor of eight, which means that the feature vector will have a size of /(8*8)= These vectors are then normalized to zero mean and unit variance.
. To extract GF, we let ………..
. down-sampled the Gabor representation O(x, y) by a factor of eight.
. The downsampling factors are changed from 4 to 16, and the downsampling factor of 8 produced the highest accuracy for the data set.
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18. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
Real parts of Gabor filters used during the feature extraction
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19. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
Gabor representation of the SAR image convolved with the real part of Gabor filter
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20. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
Framework of the proposed method
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21. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
BoVW feature representation and classification
1- codebook construction
A robust and discriminative codebook is generated by clustering the Gabor feature vectors of the training data set. For the sake of implementation simplicity and lower complexity k-means clustering algorithm is exploited. The k-means algorithm seeks to find clusters that minimize the objective function:
where the centroid of the cluster is denoted by mc, and the number of visual words (i.e., the k values) is dependent on the training dataset used.
. To generate a robust and discriminative codebook based on clustering the Gabor feature of the training data set, we adopt K-means that starting with a set of randomly chosen initial centers.
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22. THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION
BoVW feature representation and classification
2- Feature encoding and classification
The closest Euclidian distance between Gabor feature vectors of the training and testing sets, and the constructed vocabulary are computed forming new robust bag of features that represent all the targets.
Finally, the linear SVM is trained to be able to classify unknown targets.
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23. Experimental Results and Analysis
Dataset (Selected SAR images)
Moving and Stationary Target Acquisition and Recognition (MSTAR) public mixed target dataset is used for the evaluation of the system.
All SAR images are with 1-foot resolution collected by Sandia National Laboratory (SNL). They are collected using the STARLOS X-band SAR sensor in a spotlight mode with a circular flight path from diverse depression angles.
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24. Number of training and testing samples used in the experiments
Experimental Results and Analysis
Experimental results
The effectiveness of the proposed method is evaluated on three different vehicle targets from the dataset: 2S1, D7, and SLICY.
In the total, we used 822 sample images with 15° of depression angle for testing, and for the training, we used 896 sample images with 17° of depression angle as illustrated in this table.
Target
Train
Test
Depression
No.Images
2S1
17°
299
15°
274 D7
SLICY
298
Number of training and testing samples used in the experiments
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25. Experimental Results and Analysis
- The classification accuracies with dictionary sizes from 10 to 100 are compared and the experiments showed that the feature vector of 70 achieved the highest accuracy rate.
. We evaluated our proposed approach with different dictionary sizes from 10 to 100.
. The down sampling factors are changed from 4 to 16, and the down sampling factor of 8 produced the highest accuracy for the data set.
Effect of the dictionary size on the classification accuracy
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26. Experimental Results and Analysis
Confusion matrix of the classification performance
And we can see the Confusion matrix of the classification performance
The rows and columns of the matrix indicate the actual and predicted classes, respectively.
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27. Experimental Results and Analysis
Classification performance
- We evaluate the classification accuracy of the proposed method and compare with other approaches.
this process is repeated five times
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28. Experimental Results and Analysis
The classification time complexity comparison
. The proposed method has a low-complexity time due to the small size of the used feature vectors (i.e., 70).
The classification time comparison between the proposed method
and the state-of-the-art methods.
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29. CONCLUSIONS

30. CONCLUSIONS Define Synthetic Aperture Radar. SAR
Mode of SAR that is used (spotlight mode)
Motivation
Why SAR images? (Classification)
Define the problems. (Feature extraction)
Proposed
A robust feature extraction method is proposed, which takes advantages of BoVW to precisely describe the targets in complex SAR images.
Experiments on MSTAR public release dataset are conducted, and the classification accuracy and time complexity results demonstrate that the proposed method outperforms the state-of-the-art methods.
Results
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31. THANKS for your ATTENTION