Multispectral principal component imaging

Himadri S. Pal, Mark A. Neifeld

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


We analyze a novel multispectral imager that directly measures the principal component features of an object. Optical feature extraction is studied for color face images, multi-spectral LANDSAT-7 images, and their grayscale equivalents. Blockwise feature extraction is performed that exploits both spatial and spectral correlation, with the goal of enhancing feature fidelity (i.e., root mean square error). The effect of varying block size, number of features, and detector noise is studied in order to quantify feature fidelity and optimize reconstruction performance. These results are compared with conventional imaging and demonstrate the advantages of the multiplexed approach. Specifically, we find that in addition to reducing the number of detectors within the imager, the reconstruction fidelity (i.e., root mean square error) can be significantly improved using a feature-specific imager.

Original languageEnglish (US)
Pages (from-to)2118-2125
Number of pages8
JournalOptics Express
Issue number18
StatePublished - Sep 2003

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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