Shadowed object detection for hyperspectral imagery

R. Mayer; J. Antoniades; M. Baumback; D. Chester; J. Edwards; A. Goldstein; D. Haas; S. Henderson

doi:10.1117/12.738408

26 September 2007 Shadowed object detection for hyperspectral imagery

R. Mayer, J. Antoniades, M. Baumback, D. Chester, J. Edwards, A. Goldstein, D. Haas, S. Henderson

Proceedings Volume 6678, Infrared Spaceborne Remote Sensing and Instrumentation XV; 66780L (2007) https://doi.org/10.1117/12.738408
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

Objects shielded from direct illumination, or lying in shadows, can be difficult to detect using airborne hyperspectral sensors. Diminished illumination of objects reduces the signal contrast with respect to the background and shade shifts the spectral signature distribution. Supervised detection of shadowed objects is therefore confounded from implementing the simplest approach, namely inserting signatures trained on fully illuminated objects into target searches. Previously developed statistical temporal transformation ("whitening/dewhitening") of target signatures and target covariance matrices has been adapted to convert fully illuminated signatures to the more appropriate shadowed signatures for target detection. The choice of areas to transform the signatures must include dimilar background composition under full illumination and shadow conditions. A new search algorithm, Regularized Maximum Likelihood Clustering (RMLC), uses pixels for the CV computation associated with the object. "Regularizing" the object's covariance matrix avoids non-singularities from the CV computation and mitigates statistical degradation for the covariance matrix calculation due to undersampling of the small number of pixels. To accurately compute the required covariance matrices from imagery of small open and shadowed areas, "regularization" is also applied to the covariance matrices associated with those areas. The searches are applied to visible/near IR data collected from forested areas. Inserting the transformed signatures into RMLC and the adaptive cosine estimator (ACE) achieved higher target detection for fixed alarm rate, relative to the matched filter. The temporal transform of the signatures was compared to a scaling approach using mean signatures from the open and shadowed areas. This study successfully extracted targets from shadows by using a sensitive target search and through transforming signatures collected from fully illuminated conditions into shadowed spectra.

Citation Download Citation

R. Mayer, J. Antoniades, M. Baumback, D. Chester, J. Edwards, A. Goldstein, D. Haas, and S. Henderson "Shadowed object detection for hyperspectral imagery", Proc. SPIE 6678, Infrared Spaceborne Remote Sensing and Instrumentation XV, 66780L (26 September 2007); https://doi.org/10.1117/12.738408

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