Feature selection for spectral sensors with overlapping noisy spectral bands

Biliana Paskaleva, Majeed M. Hayat, J. Scott Tyo, Zhipeng Wang, Monica Martinez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Quantum-dot infrared photodetectors (QDIPs) are emerging as a promising technology for midwave- and longwave-infrared remote sensing and spectral imaging. One of the key advantages that QDIPs offer is their bias-dependent spectral response, which is brought about by the asymmetric bandstructure of the dot-in-a-well (DWELL) configuration. Photocurrents of a single QDIP, driven by different operational biases can, therefore, be viewed as outputs of different bands. It has been shown that this property, combined with post-processing strategies (applied to the outputs of a single sensor operated at different biases), can be used to perform adaptive spectral tuning and matched filtering. However, unlike traditional sensors, bands of a QDIP exhibit significant spectral overlap, an attribute that calls for the development of novel methods for feature selection. Additionally, the presence of detector noise further complicates such feature selection. In this paper, the theoretical foundations for discriminant analysis, based on spectrally adaptive feature selection, are developed and applied to data obtained from QDIP sensors in the presence of noise. The approach is based on a generalized canonical-correlation-analysis framework that is used in conjunction with an optimization criterion for the selection of feature subspaces. The criterion ranks the best linear combinations of the overlapping bands, providing minimal energy norm (a generalized Euclidean norm) between the centers of classes and their respective reconstructions in the space spanned by sensor bands. Experiments using ASTER-based synthetic QDIP data are used to illustrate the performance of rock-type Bayesian classification according to the proposed feature-selection method.

Original languageEnglish (US)
Title of host publicationAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
DOIs
StatePublished - Sep 20 2006
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII - Kissimmee, FL, United States
Duration: Apr 17 2006Apr 20 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6233 II
ISSN (Print)0277-786X

Other

OtherAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
CountryUnited States
CityKissimmee, FL
Period4/17/064/20/06

Keywords

  • Adaptive feature selection
  • Canonical-correlation analysis
  • Dot-in-a-well
  • Noise
  • Overlapping spectral bands
  • Quantum-dot infrared photodetectors
  • Rock classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Paskaleva, B., Hayat, M. M., Tyo, J. S., Wang, Z., & Martinez, M. (2006). Feature selection for spectral sensors with overlapping noisy spectral bands. In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII [623329] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6233 II). https://doi.org/10.1117/12.666773