Morphological Algorithms for Land Mine Detection

Sinan Batman, Ulisses Braga-Neto and John Goutsias

Center for Imaging Science

The Johns Hopkins University

Baltimore, MD 21218

The second method, referred to here as MM-MNF (Fig. 4), is based on a

different linear prefilter: the Maximum Noise Fraction (MNF) transform.

The MNF transform is an optimal linear filter that maximizes the signal to

noise ratio (SNR) in each transform band subject to orthogonality. The

results from this linear stage is again submitted to the morphological

detection module shown in Fig. 5.

Two hybrid algorithms,

which combine the

decorrelating qualities of

a linear filter and the

shape extracting

properties of

Mathematical

Morphology, are

investigated in the

framework of land mine

detection.

The first algorithm, referred to here as PC-MM (Fig. 3), solely operates on

the peaks in the image that are extracted by a morphological top-hat

transform. These excerpted multi-spectral peaks are then compressed

and decorrelated via the principal component (PC) transform. Due to the

packing property of the PC transform, the target markers are typically found

in the first or second bands in the PC transformed image. The targets are

then detected using a morphological detection scheme (Fig. 5).

For the target signatures to be mapped to the

band with the highest (SNR), the MM-MNF

algorithm requires an accurate estimation of the

clutter covariance. This is achieved by using

the more stable PC-MM algorithm in a first pass.

The extracted targets from this first pass, are

then used to improve the detection result in

subsequent iterations using the MM-MNF

algorithm, by updating covariance estimates of

relevant filter variables (Fig. 6).

Unmanned AerialVehicle (UAV)

GPS Satellite

Field of View

Land Mines

Figure 1. Multi-spectral aerial imaging of land mines.

Band 1

Band 6Band 5Band 4

Band 3Band 2

Figure 2. Gray scale intensity profiles of multi-spectral image bands.

Top-hat byReconstruction

HistogramStretching Thresholding

Openingby

Reconstruction

Majority

Voting

Morphological

Reconstruction

Bandwise

Union

Masks

R

Structuring element Threshold level Structuring elementCut-off parameter

Morphological Detection (Direct MM Algorithm)

Figure 5.

Multispectral

Signal Ma

rke

rs

Binary

Detection

Result

The MM-MNF Algorithm

Opening byReconstruction

MNFTransform

Structuring element

Multispectral

Signal

BandSelection(Band 6)

Direct MMDetection

R

Binary

Detection

ResultClutter

Approximation

Figure 4.

PC-MM

MM-MNF

R1

MM-MNF

R3

R2

R2

R1

Detection Detection Image

After Binarizarion

Figure 6. Iterative implementation of Morphological Detection scheme

Multispectral

SignalMissed Target

Correct Detection

False Alarm

Iteration 1

Iteration 3

Iteration 2

Top-hat byReconstruction

PrincipalComponent

Decomposition

Structuring element

BandSelectionBand 1 orBand 2

Direct MMDetection

R

The PC-MM Algorithm

Multispectral

Signal

Binary

Detection

Result

Figure 3.