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 Amrani.lmcice@hotmail.com School of Computer Science and Technology, Harbin Institute of Technology, China.

TABLE OF CONTENTS INTRODUCTION MOTIVATION THE PROPOSED METHOD EXPERIMENTAL RESULTS CONCLUSIONS

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

INTRODUCTION Synthetic Aperture Radar is a high-resolution airborne and spaceborne remote sensing system for imaging remote targets on a terrain. ICDIP2017 © Amrani Moussa 2017

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 ICDIP2017 © Amrani Moussa 2017

INTRODUCTION Modes of SAR San SAR Spotlight SAR Side-looking SAR ICDIP2017 © Amrani Moussa 2017

INTRODUCTION Modes of SAR San SAR Spotlight SAR Side-looking SAR ICDIP2017 © Amrani Moussa 2017

MOTIVATION Why SAR images? Define the problems.

MOTIVATION Military surveillance Why SAR images ? SAR has been primarily utilized for many applications on a target such as: Military surveillance ICDIP2017 © Amrani Moussa 2017

MOTIVATION Reconnaissance Why SAR images ? SAR has been primarily utilized for many applications on a target such as: Reconnaissance ICDIP2017 © Amrani Moussa 2017

MOTIVATION Classification Why SAR images ? SAR has been primarily utilized for many applications on a target such as: Classification ICDIP2017 © Amrani Moussa 2017

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 ICDIP2017 © Amrani Moussa 2017

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). ICDIP2017 © Amrani Moussa 2017

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. ICDIP2017 © Amrani Moussa 2017

THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION Framework of the proposed method ICDIP2017 © Amrani Moussa 2017

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 ICDIP2017 © Amrani Moussa 2017

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=1048576. 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 1048576/(8*8)=16384. 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. ICDIP2017 © Amrani Moussa 2017

THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION Real parts of Gabor filters used during the feature extraction ICDIP2017 © Amrani Moussa 2017

THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION Gabor representation of the SAR image convolved with the real part of Gabor filter ICDIP2017 © Amrani Moussa 2017

THE PROPOSED METHOD FOR SAR TARGET CLASSIFICAION Framework of the proposed method ICDIP2017 © Amrani Moussa 2017

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. ICDIP2017 © Amrani Moussa 2017

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. ICDIP2017 © Amrani Moussa 2017

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. ICDIP2017 © Amrani Moussa 2017

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 ICDIP2017 © Amrani Moussa 2017

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 ICDIP2017 © Amrani Moussa 2017

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. ICDIP2017 © Amrani Moussa 2017

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 ICDIP2017 © Amrani Moussa 2017

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. ICDIP2017 © Amrani Moussa 2017

CONCLUSIONS

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 ICDIP2017 © Amrani Moussa 2017

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