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This course delves into advanced techniques for target detection in remote sensing, focusing on contemporary mapping methods. Key topics include Matched Filtering, Mixture Tuned Matched Filtering (MTMF), Constrained Energy Minimization (CEM), Adaptive Coherence Estimator (ACE), and Spectral Angle Mapper (SAM). Participants will learn how to optimally identify targets using various algorithms, each designed to enhance detection capabilities while minimizing false positives. Through practical applications and discussions, attendees will expand their skills in utilizing these advanced methodologies effectively.
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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 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.
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.
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.
