Target Detection using Advance Mapping methods

1. 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

2. 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)

3. Matched Filtering • Matched Filtering to find the abundances of user-defined 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.

4. 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.

5. 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.

6. MTMF (Mixture-Tuned Matched Filtering )

7. 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.

8. 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.

9. Questions & Discussion