METHODS FOR TARGET DETECTION IN SAR IMAGES

Transcription

1 METHODS FOR TARGET DETECTION IN SAR IMAGES Kaan Duman Supervisor: Prof. Dr. A. Enis Çetin December 18, 2009 Bilkent University Dept. of Electrical and Electronics Engineering

2 Outline Introduction Target Detection in SAR Images using Region Covariance (RC) and Codifference Using various distance metrics and SVM Classifiers Comparisons with PCA method Use of Directional Filters (DFs) on Target Detection Methods l 1 norm distance metric SVM classifiers Conclusions

3 Introduction Synthetic Aperture Radar (SAR) sensors can produce images of scenes in all weather conditions at any time of day and night. Many algorithms are developed for SAR ATR (Automatic Target Recognition) algorithms are provided in the literature. Methods presented here can be used for detection or discrimination stage of SAR ATR. Region covariance and codifference matrix features are used in the SAR ATR target detection problem. SAR images are pre-processed with directional filters in order to decrease the computational cost of the algorithm. Experimental results using the MSTAR SAR database are presented at the end of each part.

4 PART I Target Detection in SAR Images Using Region Covariance (RC) and Codifference

5 Feature Vector Extraction of ROI For each pixel in the region of interest (ROI): z k = x y I(x,y) where, di(x, y) dx di(x, y) dy d 2 I(x, y) dx 2 d 2 I(x, y) dy 2 T z k = [ z k (i)] T i is the index of the feature vector (i = 1,2,,7), k is the label of a pixel, (x, y) is the position of a pixel, I is intensity of a pixel (since gray-scale images are used in this study), d(i(x,y)) /dx is the horizontal and d(i(x,y))/dy is the vertical first derivative of the ROI calculated with the filter [-1 0 1], d 2 (I(x,y))/dx 2 is the horizontal and d 2 (I(x,y))/dy 2 is the vertical second derivative of the ROI calculated with the filter [-1 2-1], Example: For a 4 by 4 region, k = 1,2, 16; x = 1,2,3,4; y = 1,2,3,4.

6 Region Covariance (RC) Matrix where, n is the total number of pixels in the ROI c R (i,j) is the (i, j) th component of the covariance matrix Advantages of using RC matrix: Invariant to a degree of scale and illumination change Provides an averaging filter to filter out the naturally occurring speckle noise in SAR images.

7 Region Codifference Matrix C R = [ c R (i, j) ] = 1 n 1 n k=1 z k (i) z k ( j) 1 n n k=1 n z k (i) z k ( j) k=1 where, the scalar multiplication is replaced by an additive operator. The operator is basically a summation operation but the sign of the result behaves similar to the multiplication operation: for real numbers a and b.

8 Stages of the Target Detection Algorithm Used distance metrics: Involving computation of eigenvalues: 1) Gen. Eig. Dist. Met. 2) Dis. Eig. Dist. Met. p ρ 2 (C 1,C 2 ) = log 2 (λ i (C 1 ) λ i (C 2 )) i=1 Based on l 1 norms: 3) l 1 Norm Dist. Met. 4) Norm. l 1 Norm Dist. Met. p p ρ 3 (C 1,C 2 ) = ( C 1 (i, j) C 2 (i, j) ) ρ i =1 j =1 4 (C 1,C 2 ) = ρ 5 (C 1,C 2 ) = p i =1 p j =1 p i =1 C 1 (i, j) C 2 (i, j) (C 1 (i,i) + C 2 (i,i)) p 5) 2 nd Norm. l 1 Norm Dist. Met. j =1 C 1 (i, j) C 2 (i, j) C 2 (i,i) where, C 1, C 2 are the covariance or codifference matrices of regions R 1 (from test images) and R 2 (from training images), respectively.

9 Stages of the Target Detection Algorithm A sample covariance or codifference matrix where the bounded region shows the necessary features for the decision algorithm:

10 MSTAR SAR Database Examples of 128-by-128 and cropped 64-by-64 target and clutter images (ROIs):

11 MSTAR SAR Database Number of images used in the training and testing studies: Experimental results include: Detection Accuracy: (number of correctly detected target images) / (number of total test images) False Alarm: (number of clutter images detected as target images) / (number of total clutter images)

12 Experimental Results The target detection accuracies (%) obtained with the RC and codifference methods using defined distance metrics and SVM classifiers: Decision Methods Preferred: Using region covariance (RC) method 128 by 128 images 64 by 64 images SVM classifiers 2 nd Norm. l 1 norm dist. Norm. l 1 norm dist. l 1 norm dist. met. Dis. eig. dist. met. Gen. eig. dist. met Target detection accuracies (%) Using region codifference method 128 by 128 images 64 by 64 images SVM classifiers 6 2 nd Norm. l 1 norm dist. 5 Norm. l 1 norm dist. l 1 norm dist. met. Dis. eig. dist. met. Gen. eig. dist. met Target detection accuracies (%)

13 Experimental Results Decision Methods Preferred: The false alarm rates (%) obtained with the RC and codifference methods using defined distance metrics and SVM classifiers: Using region covariance (RC) method SVM classifiers 6 2 nd Norm. l 1 norm dist. 5 Norm. l 1 norm dist. 4 l 1 norm dist. met. 3 Dis. eig. dist. met. 2 Gen. eig. dist. met by 128 images 64 by 64 images False alarm rates (%) Using region codifference method SVM classifiers 6 2 nd Norm. l 1 norm dist. 5 Norm. l norm dist. 1 4 l 1 norm dist. met. 3 Dis. eig. dist. met. 2 Gen. eig. dist. met by 128 images 64 by 64 images False alarm rates (%)

14 Using k-nn (k Nearest Neighbor) Algorithm The target detection accuracies (%) obtained for increasing k values using l 1 norm distance metric on RC and codifference methods: Target detection accuracies (%) Using RC method on 128 by 128 images Using codifference method on 128 by 128 images Using RC method on 64 by 64 images Using codifference method on 64 by 64 images k values

15 Using k-nn (k Nearest Neighbor) Algorithm The false alarm rates (%) obtained for increasing k values using l 1 norm distance metric on RC and codifference methods: Using RC method on 128 by 128 images Using codifference method on 128 by 128 images Using RC method on 64 by 64 images Using codifference method on 64 by 64 images 10 False alarm rates (%) k values

16 Using PCA (Principal Component Analysis) Number of images used in training and testing studies for the PCA method Target detection accuracies and false alarm rates achieved using PCA method

17 Comparisons with Commonly Used PCA Method Target detection accuracies and false alarm rates achieved with the new database: Using l 1 norm distance metric Using SVM as a classifier

18 PART II Use of Directional Filters (DFs) on Target Detection Methods

19 The Two-Stage Target Detection System ROIs in SAR images are filtered using 2-D directional filters (DFs) in the pre-processing stage. After the images are put into sub-categories by the output of the DFs, they are classified with the RC and codifference methods using l 1 norm distance metric and SVM classifiers.

20 Directional Filters (DFs) Used to classify target and clutter images in the database according to their orientations in the pre-processing stage. Distributing images into sub-categories decreases the number of distance calculation based on l 1 norms. Design of the DFs:

21 Block Diagram of the Pre-processing Stage

22 Target Detection Strategy Block diagram of the target detection stage applied in each sub-category using (a) l 1 norm distance metric (b) SVM classifiers

23 Experimental Results Sample 64-by-64 target images selected by DFs 1-10

24 Experimental Results The results of various two-stage set-ups working on 10 sub-categories

25 Experimental Results The results of various two-stage set-ups working on 8 sub-categories

26 Experimental Results The total target detection accuracies (%) obtained with the two-stage set-ups for different numbers of sub-categories Target detection accuracies (%) Using RC method with l 1 norm distance metric Using RC method with SVM classifiers Using codifference method with l 1 norm distance metric Using codifference method with SVM classifiers Number of sub categories

27 Experimental Results The total false alarm rates (%) obtained with the two-stage setups for different numbers of sub-categories 1.4 Using RC method with l 1 norm distance metric Using RC method with SVM classifiers Using codifference method with l 1 norm distance metric Using codifference method with SVM classifiers False alarm rates (%) Number of sub categories

28 Experimental Results The results of various two-stage set-ups working on 10 subcategories with the new database

29 Using k-nn (k Nearest Neighbor) Algorithm The total target detection accuracies (%) obtained for increasing k values using 10 sub-categories for the two-stage system. l 1 norm distance metric is used. 100 Using RC method Using codifference method 99.5 Target detection accuracies (%) k values

30 Using k-nn (k Nearest Neighbor) Algorithm The total false alarm rates (%) obtained for increasing k values using 10 sub-categories for the two-stage system. l 1 norm distance metric is used. 9 8 Using RC method Using codifference method 7 False alarm rates (%) k values

31 Conclusions The use of RC and codifference methods for SAR image classification problems are investigated using various distance metrics and SVM classifiers. To further reduce the computational complexity of the algorithm, DFs are used in a pre-processing stage, in order to divide the images into sub-categories. The codifference methods provide higher detection accuracies and lower false alarm rates than RC methods, besides having lower computational costs. The distance metrics involving l 1 norms provide superior classification results than other metrics used. For real-time applications, the SAR image classification method based on SVM using codifference features has the lowest computational complexity.