Document Type
Article
Language
eng
Publication Date
10-2008
Publisher
Institute of Electrical and Electronic Engineers (IEEE)
Source Publication
IEEE Transactions on Geoscience and Remote Sensing
Source ISSN
0196-2892
Abstract
The main focus of this paper is a rigorous development and validation of a novel canonical correlation feature- selection (CCFS) algorithm that is particularly well suited for spectral sensors with overlapping and noisy bands. The proposed approach combines a generalized canonical correlation analysis framework and a minimum mean-square-error criterion for the selection of feature subspaces. The latter induces ranking of the best linear combinations of the noisy overlapping bands and, in doing so, guarantees a minimal generalized distance between the centers of classes and their respective reconstructions in the space spanned by sensor bands. To demonstrate the efficacy and the scope of the proposed approach, two different applications are considered. The first one is separability and classification analysis of rock species using laboratory spectral data and a quantum-dot infrared photodetector (QDIP) sensor. The second application deals with supervised classification and spectral unmixing, and abundance estimation of hyperspectral imagery obtained from the Airborne Hyperspectral Imager sensor. Since QDIP bands exhibit significant spectral overlap, the first study validates the new algorithm in this important application context. The results demonstrate that proper postprocessing can facilitate the emergence of QDIP-based sensors as a promising technology for midwave- and longwave-infrared remote sensing and spectral imaging. In particular, the proposed CCFS algorithm makes it possible to exploit the unique advantage offered by QDIPs with a dot-in-a-well configuration, comprising their bias-dependent spectral response, which is attributable to the quantum Stark effect. The main objective of the second study is to assert that the scope of the new CCFS approach also extends to more traditional spectral sensors.
Recommended Citation
Paskaleva, Biliana S.; Hayat, Majeed M.; Wang, Zhipeng; Tyo, J. Scott; and Krishna, Sanjay, "Canonical Correlation Feature Selection for Sensors with Overlapping Bands: Theory and Application" (2008). Electrical and Computer Engineering Faculty Research and Publications. 592.
https://epublications.marquette.edu/electric_fac/592
ADA Accessible Version
Comments
Accepted version. IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 10 (October 2008): 3346-3358. DOI. © 2008 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.
Majeed M. Hayat was affiliated with the University of New Mexico, Albuquerque at the time of publication.