Analytical comparison of the matched filter and orthogonal subspace projection detectors in structured models for hyperspectral images

Peter Bajorski

doi:10.1117/12.663591

4 May 2006 Analytical comparison of the matched filter and orthogonal subspace projection detectors in structured models for hyperspectral images

Peter Bajorski

Author Affiliations +

Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII; 623301 (2006) https://doi.org/10.1117/12.663591
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

In this paper, we perform an analytical comparison of two well-known detectors-the matched filter detector (MFD) and the orthogonal subspace projection (OSP) detector for the subpixel target detection in hyperspectral images under the assumption of the structured model. The OSP detector (equivalent to the least squares estimator) is a popular detector utilizing background signature information. On the other hand, the MFD is intended for a model without background information, and it is often used for its simplicity. The OSP detector seems to be more reliable because it removes the interference of background signatures. However, it has been demonstrated in the literature that sometimes the MFD can be more powerful. In this paper, we show analytical results explaining the relationship between the two detectors beyond the anecdotal evidence from specific hyperspectral images or simulations. We also give some guidelines on when the MFD may be more beneficial than the OSP, and when the OSP is better because of being more robust against a wide range of conditions.

Citation Download Citation

Peter Bajorski "Analytical comparison of the matched filter and orthogonal subspace projection detectors in structured models for hyperspectral images", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623301 (4 May 2006); https://doi.org/10.1117/12.663591

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