TARGET DETECTION USING
ADVANCE MAPPING METHODS
Mirza Muhammad Waqar
Contact:
mirza.waqar@ist.edu.pk
+92-21-34650765-79 EXT:2257
RG712
Course: Special Topics in Remote Sensing & GIS
Outlines

Matched Filtering (MF)

Mixture Tuned Matched Filtering (MTMF)

Constrained Engery Minimization (CEM)

Adaptive Coherence Estimator (ACE)

Spectral Angle Mapper (SAM)

Orthogonal Subspace Projection (OSP)

Target Constrained Interference-Minized Filter (TCIMF)

Mixture Tuned TCIMF (MTTCIMF)
Matched Filtering



Matched Filtering to find the abundances of userdefined endmembers using a partial unmixing.
Not all of the endmembers in the image need to be
known.
This technique maximizes the response of the
known endmember and suppresses the response of
the composite unknown background, thus matching
the known signature.
Match Filtering

It provides a rapid means of detecting specific
materials based on matches to library or image
endmember spectra
 Does
not require knowledge of all the endmembers
within an image scene.

This technique may find some false positives for
rare materials.
MTMF (Mixture-Tuned Matched Filtering )


Is a hybrid method based on the combination of the
matched filter method (no requirement to know all
the endmembers) and linear mixture theory.
The results are two images:


a MF score image with 0 to 1 (1 is perfect match), and
A infeasibility image, the smaller the better match.



Infeasibility is based on both noise and image statistics and indicates
the degree to which the Matched Filtering result is a feasible mixture
of the target and the background.
Pixels with high infeasibilities are likely to be false positives
regardless of their matched filter value.
Use 2-D scatter plot to locate those pixels in an image.
MTMF (Mixture-Tuned Matched Filtering )
Spectral Angler Mapper (SAM)


Matches image spectra to reference target spectra in
n dimensions. SAM compares the angle between the
target spectrum (considered an n-dimensional
vector, where n is the number of bands) and each
pixel vector in n-dimensional space.
Smaller angles represent closer matches to the
reference spectrum. When used on calibrated data,
this technique is relatively insensitive to
illumination and albedo effects.
Orthogonal Subspace Projection (OSP)


OSP first designs an orthogonal subspace projector
to eliminate the response of non-targets, then
applies MF to match the desired target from the
data.
OSP is efficient and effective when target signatures
are distinct. When the spectral angle between the
target signature and the non-target signature is
small, the attenuation of the target signal is
dramatic and the performance of OSP could be poor.
Questions & Discussion