Interference rejection approach to noise adjusted principal components transform

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2 Citations (Scopus)

Abstract

A signal-to-noise ratio based PCA approach, called Maximum Noise Fraction (MNF) transformation or Noise Adjusted Principal Components (NAPC) transform PCA was recently developed to arrange principal components in decreasing order of image quality rather than data variance as done for PCA. One of major disadvantages of this approach is that the noise covariance matrix must be estimated accurately from the data a priori. Another is that the factor of interference is not taken into account in MNF or NAPC where the effect of interference tends to be more serious than noise in hyperspectral images. In this paper, these two problems are addressed by considering the interference as a separate unwanted signal source from which an interference rejection approach to noise adjusted principal components transform (IRNAPC) can be developed in a similar manner that the NAPC was derived. It is shown that if interference is taken care of properly, IRNAPC significantly improves NAPC. Additionally, interference annihilation also improves the estimation of the noise covariance matrix.

Original languageEnglish
Pages2059-2061
Number of pages3
Publication statusPublished - 1998
EventProceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5) - Seattle, WA, USA
Duration: 1998 Jul 61998 Jul 10

Conference

ConferenceProceedings of the 1998 IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Part 1 (of 5)
CitySeattle, WA, USA
Period98-07-0698-07-10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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