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Efficient mine detection using wavelet PCA and morphological top

hat filtering

Nizam U. Chowdhury and Mohammad S. Alam

Department of Electrical and Computer Engineering, University of South Alabama

307 N. University Blvd., Mobile, AL 36688

[email protected], [email protected]

ABSTRACT

An efficient unsupervised technique is proposed for land mine detection from highly cluttered inhomogeneous

environment. The proposed technique uses multispectral data for which feature extraction is necessary to classify

large volume of data. We applied wavelet based principal component analysis to reduce the dimension of the data as

well as to reveal information about target from background clutter. To increase the discrimination between target

and clutter a linear transformation of the feature extracted bands is performed. Thereafter, morphological algorithm

is used to extract the maximum information about the target. The proposed technique shows excellent detection

performance while enhancing the processing speed. Test results using various multispectral data sets show excellent

performance and verify the effectiveness of the proposed technique.

Keywords: Mine detection, multispectral imagery, coastal battlefield reconnaissance and analysis (COBRA),

wavelet, principal component analysis (PCA), mathematical morphology

1. INTRODUCTION

Land mine detection is an area of intense research due to its important implications in humanitarian and battlefield

related issues [1]. The objective is to develop target detection algorithms that provide automatic detection of land

mines with better accuracy. U.S. Marine Corps Advanced Technology Program sponsored such a program for

minefield detection called Coastal Battlefield Reconnaissance and Analysis (COBRA) [2]. The COBRA system

consists of a spinning filter wheel multispectral intensified video camera, mounted on an unmanned aerial vehicle

that flies over areas of interest collecting imagery of land mines [1]. This sensor captures images of a particular

scene at six different wavelengths in a spectral region from 400 nm to 900 nm [3], and provide spectral information

about the artificial targets and natural objects which is recorded in the form of a three-dimensional data cube.

In [4], Clark used probabilistic neural network classifier (PNN) to classify image regions as mine or background.

The PNN is trained with a set of mines and background tiles from the associated ground truth. One limitation of this

method is the requirement of large training data and a huge memory management to meet the desired probability of

detection and false alarms requirements. Another recently reported technique uses high dimensional generalized

discriminant algorithm to extract feature from multispectral data [5]. Then the pixels are classified with a nearest

neighbor (NNB) classifier. But this method also requires training where half of the data samples are used for training

and the rest are used for classification. Islam et al. [6] proposed a matched filtering based algorithm, which uses a

sliding concentric window (SCW) technique for postprocessing. This algorithm also shows many false alarms in

some scenarios of COBRA data. However, an adaptive algorithm [7] has been proposed for detecting mines in

COBRA data, which does not require any a priori knowledge of mine signatures. The algorithm shows satisfactory

performance, achieving around 74% correct detection. In [8], another algorithm was proposed, which yields 94%

accuracy with the COBRA data. But the drawback of this algorithm is that it requires human intervention, since the

MNF bands to be preserved needs to be chosen manually.

Optical Pattern Recognition XXIV, edited by David Casasent, Tien-Hsin Chao, Proc. of SPIE Vol. 8748, 87480Q · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2018251

Proc. of SPIE Vol. 8748 87480Q-1

Downloaded From: http://proceedings.spiedigitallibrary.org/ on 10/03/2013 Terms of Use: http://spiedl.org/terms

x(m,n)

h(

low -pass

g(m,n)

high -pass

h(m.n)

g(m,n)

h(m,n)

g(m,n)

21

LL

LH

-HL

decimation along rows decimation along columns

In this paper, we propose an automatic land mine detection technique which does not require any data training and

shows excellent performance. The proposed algorithm uses wavelet based PCA to reduce the dimension of the data

as well as to reveal information about target from background clutter. To increase the discrimination between target

and clutter, a linear transformation of feature extracted bands is performed. Thereafter, histogram stretching and

morphological algorithm are used to extract the maximum information of target.

2. WAVELET TRANSFORM

Wavelet transform is a mathematical tool to perform multiresolution analysis (MRA) of a signal. Scientists and

researchers have been using wavelets in many successful applications like noise removal, data compression, and

texture classification. The wavelet transform may be either continuous or discrete. The Continuous Wavelet

Transform (CWT) is provided by the following equation,

(a,b) =

Ψ *

dt, . (1)

where, x(t) is the signal to be analyzed, Ψ(t) is the mother wavelet or the basis function, a is scaling parameter, and b

is shifting parameter. All the wavelet functions used in the transformation are derived from the mother wavelet

through shifting and scaling. The shifting parameter relates to the location of the wavelet function as it is shifted

through the signal, therefore, it corresponds to the time information in the wavelet transform. And the scaling

parameter corresponds to frequency information.

The discrete wavelet transform (DWT) is a highly efficient alternative to the CWT that transforms a discrete time

signal to a discrete wavelet representation. It provides a compact representation of a signal’s frequency components

with strong spatial support. DWT decomposes a signal into frequency subbands at different scales from which it can

be perfectly reconstructed. DWT of a signal can be implemented by passing the signal through a filter banks of low-

pass and high-pass filters followed by downsamplers.

All images are considered as two dimensional signals. Therefore, two dimensional discrete wavelet transform (2D-

DWT) can be applied on images. 2D-DWT is implemented by following the similar procedure of 1D-DWT but it

requires an extra step at each level of decompositions. In 1D analysis, only two subband are generated at each level

of decompositions. But in 2D analysis, we get four subbands at each level of decompositions. 2D-DWT can be

implemented using 1D-DWT twice; fist time along the rows and second time along the columns or vice versa. Fig. 1

represents the implementation of 2D-DWT on image x.

Fig. 1: Discrete wavelet transformation.

A two-dimensional representation of wavelet decomposition can be shown as Fig. 2.



LL HL

LH HH

LL HL

HL

1

LH HH

LH HH

a) Decomposition level 1 b) Decomposition level 2

Fig. 2: Two dimensional representation of wavelet decomposition: (a) decomposition level 1, (b) decomposition level 2.

3. PRINCIPAL COMPONENT ANALYSIS

PCA is a mathematical technique that converts a set of observations of possibly correlated variables into a set of

values of uncorrelated variables by orthogonal transformation. The goal of PCA is to extract important information

from data to represent it as a set of new variables called principal components, and to display the pattern of

similarity of the observations [9]. The number of principal components is less than or equal to the number of

original variables. This transformation is defined in such a way that the first principal component has the largest

possible variance, and each successive component has lower variance than the previous one.

PCA has been used in remote sensing for different purposes. Mather (1999) summarized different applications of

PCA, including correlation analysis of Landsat TM images for effective feature recognition and identification of

areas of change with multitemporal images [9]. A brief mathematical procedure of PCA for multispectral image

analysis has been shown below.

We consider an N bands multispectral image data set where each band contains with m rows and n columns of

pixels. PCA is applied on two dimensional data matrix, therefore, it is necessary to convert the 3D multispectral

image data into 2D data matrix. After transformation, the new data matrix will have m n rows and N columns,

which means that each of the image bands has been converted to a column vector in 2D data matrix.

We name the 2D original data matrix as X, and want to find the covariance matrix of this data. The covariance

matrix consists of the correlation characteristics (covariance and variances) existing among all pairs of the data set

X. The covariance between two measurements measures the degree of mutual similarity. A large absolute value

denotes a high redundancy (or correlation) of respective data. On the other hand, zero covariance points to

completely uncorrelated data.

Covariance between two random variables and is defined as

, (2)

where, = E( ) is the mean or expected value of a random variable . Now the covariance matrix of X can be

computed by following:



(3)

The PCA is based on the eigenvalue decomposition of the covariance matrix, which can be decomposed to the

following form:

∑ = PDPT (4)

where, P is the orthogonal matrix composed of the eigenvectors, and D is the diagonal matrix composed of the

eigenvalues , ... of the covariance matrix ∑, expressed as

(5)

In Eq. (5), the eigenvalues are arranged in descending order so that > >... > , and the eigenvectors (columns

of P) are arranged according to their corresponding eigenvalues. The eigenvectors are the measure of variance of

the bands, and is used for finding necessary information.

Now the new PCA transformed data matrix Y can be obtained by multiplying original data matrix X by the

eigenvector matrix P, given by

Y=XP (6)

4. MATHEMATICAL MORPHOLOGY

Mathematical Morphology has become a powerful nonlinear image analysis methodology with operators capable of

handling sophisticated image processing tasks in binary and grayscale images. Our attention in this paper focuses

primarily on the filtering and target extraction capabilities of morphological operators. We consider a grayscale

image f, whose flat erosion by a structuring element b is defined as

(7)

where, denotes minimum. Similarly, the flat dilation of f by the structuring element b is defined by:

(8)

where, denotes maximum. The opening of the image f by structuring element b is defined as the erosion of f by b

followed by a dilation of the result with b:

(9)

Similarly, the closing of f by the structuring element b is defined as the dilation of f by b followed by an erosion of

the result with b:

(10)



f

Assume the intensity profile of f is similar to the intensity profile as shown in Figure 3(a) [1]. Our purpose is to

preserve the pre-selected number of peaks in signal f, while removing the rest of the signal. To do so, we create

another signal fm called marker, which identifies the portion of the signal f that needs to be removed. From Figure

3(a), we see that marker fm marks the whole signal except the first 2 peaks of f, which means that we are interested to

preserve only the first 2 peaks by removing the whole black portion of the signal marked by fm.

(a) (b)

Fig. 3. Morphological top-hat filtering: (a) a signal f and a marker fm. (b) extraction of the peaks of f that lie above fm by means of

the top-hat by filtering.

Typically, targets in an image express themselves as peaks in the intensity profile of f. Therefore, fm marks the

clutter of the signal f, and constructs an approximation of the clutter. Now a simple subtraction of the reconstructed

clutter from the original signal f will extract the desired targets from f, which are shown at Figure 3(b). Therefore,

the target extracted signal can be expressed as following:

ftarget = f- fm (11)

The top-hat filtering can be effectively used to remove or reduce clutter and enhance the presence of targets. The

appropriate selection of the marker is possibly the most important task of using the morphological top-hat filtering in

target extraction problems. For a two dimensional signal f, marker fm can be obtained by a flat opening of f by a

structuring element b (size of b is slightly larger than the target with minimum size). Therefore, all targets are

extracted by means of top-hat filtering, which can be defined by following:

ftophat = f- (12)

Although top-hat filtering extracts all targets from background clutter, a significant portion of clutter would likely to

be present in the filtered image. However, this algorithm is still being effectively used in target detection algorithm.

5. PROPOSED ALGORITHM

The multispectral land mine dataset used in this work consists of six spectral bands which span from 400nm to

900nm with a 100nm interval. The size of each scene is approximately 352 677 pixels, and contains different

number of mines with different sizes. The minimum size of the mines is 3 3 pixels. The background is highly

cluttered and it varies from scene to scene. Therefore, developing an automated algorithm to detect all the mines was

a huge challenge.



In this paper, we proposed a new algorithm for land mine detection in multispectral imagery. The new algorithm

employs DWT as preprocessing to remove noise and reduce the dimensionality of input multispectral data. We used

only one step wavelet decomposition of the images, and calculated the approximate coefficients for each

multispectral band. Thus we get a multispectral data cube of approximate coefficients on which PCA is applied. To

apply PCA on the multispectral dataset, we consider N (in our case, N=6) bands of multispectral image dataset

where each band contains m rows and n columns of pixels. As PCA is applied on two-dimensional data matrix, it is

necessary to convert the 3D multispectral image data into 2D data matrix. After this transformation, the new data

matrix contains m n rows and N columns, which means that each of the image bands is converted into a column

vector in the 2D data matrix. Then we applied PCA on the original 2D data to generate the PCA transformed data.

The transformed 2D data was then converted to 3D multispectral image data to obtain the orthogonal bands.

Although band 1 of the transformed bands (orthogonal bands) contains the maximum information (highest variance)

of the scene, but it does not provide useful information about the target. Therefore, for feature extraction, we needed

to select those bands in which target pixel values significantly differ from the background pixel values. After

analyzing the PCA transformed bands, we found that band 2 and band 3 show significantly distinguishable target

pixel values compared to the background pixel values (darker in band 2 and brighter in band 3).

To obtain more clear information about the targets, we applied a simple band transformation by subtracting band 2

from band 3. This transformation partially removes the background clutter while brightening the target pixel values.

Then morphological top hat filtering was applied on the transformed band to minimize the background clutter while

keeping the target information intact. In some cases, we can directly apply thresholding on the top-hat filtered image

and detect the targets. But for extremely cluttered scenarios, thresholding after top-hat filtering is not an effective

way to detect all targets because of the significant amount of false alarms. Therefore, an efficient postprocessing

step is essential for robust detection of all possible targets. In the proposed algorithm, we used histogram

equalization, blurring and image multiplication. Then an automated thresholding detects all targets in the scene with

an excellent accuracy. Figure 4 shows the block diagram of the proposed algorithm.

Fig. 4. Block diagram of the proposed algorithm.

Preprocessing

PCA transformation

Input multispectral

data

Morphological top-

hat filtering

Postprocessing

Adaptive

thresholding

Detected mines



6. EXPERIMENTAL RESULTS

The proposed algorithm was applied to four different multispectral datasets bb.08.0040, bb.08.0046, bb.08.0052,

and bb.08.0058, respectively. There are six bands in all of these datasets. The targets of interest are various land

mines of different shapes and sizes. The test datasets along with their ground truth positions are shown at Fig. 5 [3].

(a) (b)

(c) (d)

Fig. 5. Ground truth positions of mines: (a) dataset bb.08.0040, (b) dataset bb.08.0046, (c) dataset bb.08.0052, and (d) dataset

bb.08.0058.

The effectiveness of the proposed algorithm was tested with datasets bb.08.0040, bb.08.0046, bb.08.0052, and

bb.08.0058, respectively. The results of the proposed algorithm for these datasets have been shown in Figs. 6-9,

respectively.

Figure 6(a) shows one band of multispectral dataset 1 (bb.08.0040). It contains 11 mines but all mines are hidden in

these images. After applying PCA on this dataset, we obtained six orthogonal bands. We chose bands 2 and 3 for

feature extraction, and applied linear transformation on these two bands. The transformed band is shown at Fig. 6(b).

Then morphological top hat filtering was applied to this transformed band, and the result is shown in Fig. 6(c). In

this algorithm, applying thresholding after the top-hat filtering is not an effective way to detect all targets in the

scene because it yields significant amount of false alarms. Therefore, an efficient post-processing step is required to

improve the performance of the algorithm. We used histogram equalization, blurring and image multiplication as the

postprocessing step. Then an automated thresholding technique was applied which can detect all the targets in the

scene with excellent accuracy. The detected mines are shown inside the white squares in Fig. 6(d). There is only one

false alarm in this image which is shown inside the circle.



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Fig. 6. (a) One band from dataset bb.08.0040, (b) image after band transformation of feature extracted bands, (c) result after using

morphological top-hat filtering, and (d) result after post-processing and thresholding.

(a) (b)

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The same algorithm was applied to the rest of the datasets and the results are shown in Figs. 7-9, respectively. For

dataset bb.08.0046, the algorithm successfully detected all targets with only one false alarm. The result for this

dataset is shown in Fig. 7(d). Then we applied the proposed algorithm to datasets bb.08.0052 and bb.08.0058, and

the results are shown in Figs. 8, and 9, respectively. From Figs. 8 and 9, it is evident that the algorithm detected all

targets without any false alarms. Therefore, the proposed algorithm is very effective even when the targets are

hidden in highly cluttered environment.

7. CONCLUSION

In this paper, we presented a new mine detection algorithm in multispectral datasets. The proposed algorithm uses

DWT to remove noise from the data as well as reduce the size of the data by sub-sampling. Then PCA is applied for

dimensionality reduction and feature extraction purposes. Linear transformation of feature extracted bands and

morphological top-hat filtering reveals targets in the image while significantly reducing the background clutter.

Thereafter, an efficient postprocessing step is applied for detecting all targets. Test result using various multispectral

datasets show excellent performance and verify the effectiveness of the proposed technique.

REFERENCES .

[1] S. Batman, J. Goutsias, “ Unsupervised iterative detection of land mines in highly cluttered environment,” IEEE

Transaction on Image Processing, vol. 12, no. 5 pp. 509-523, May 2003.

[2] H. Witherspoon, J. H. Holloway, K. S. Davis, R. W. Miller, and A. C. Dubey, “The coastal battlefield

reconnaissance and analysis (COBRA) program for minefield detection,” Proc. SPIE, vol. 2496, pp. 500–508

Apr. 17–21, 1995.

[3] D. Casasent, X. Chen, “Feature Reduction and Morphological Processing for Hyperspectral Image Data,”

Applied Optics, vol. 43, pp. 227-236, Jan 10, 2004.

[4] G. A. Clark, S. K. Sengupta, W. D. Aimonetti, F. Roeske and J. G. Donetti, “Multispectral Image Feature

Selection for Land Mine Detection,” IEEE Transaction on Geoscience and Remote Sensing, vol. 38, no. 1, pp.

304-311, 2000.

[5] D. Casasent and X. W. Chen, “Mine and Vehicle Detection in Hyperspectral Image Data: Waveband Selection,”

Proceedings of the SPIE, vol. 5094, pp. 228-241, 2003.

[6] M. M. Islam and M. S. Alam, “Mine Detection in Multispectral Imagery Using PCA and Matched Filtering,”

Proc. of SPIE vol. 6967, pp. 69670D, 2008.

[7] Q. A. Holmes, C. R. Schwartz, J. H. Seldin, J. A. Wright, and L. J.Witter, “Adaptive multispectral CFAR

detection of land mines,” Proc. SPIE, vol. 2496, pp. 421–432, Apr. 17–21, 1995.

[8] U. M. Braga-Neto and J. Goutsias, “On detecting mines and mine like objects in highly cluttered multispectral

aerial images by means of mathematical morphology,” Proc. SPIE Conf., vol. 3392, pp. 987–999, Apr. 13–17,

1998.

[9] H. Abdi, L. Williams, “Principal Component Analysis,” Wiley Interdisciplinary Reviews: Computational

Statistics, 2, in press 2010.



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