Open AccessMultispectral principal component imaging
Author(s) -
Himadri S. Pal,
Mark A. Neifeld
Publication year - 2003
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.11.002118
Subject(s) - multispectral image , principal component analysis , feature (linguistics) , artificial intelligence , computer science , pattern recognition (psychology) , detector , feature extraction , computer vision , optics , spectral imaging , full spectral imaging , noise (video) , mean squared error , pixel , physics , mathematics , image (mathematics) , statistics , philosophy , linguistics
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.