NOISE ESTIMATION OF HYPERSPECTRAL REMOTE SENSING IMAGE BASED ON MULTIPLE LINEAR REGRESSION AND WAVELET TRANSFORM

Authors

  • DONG XU College of Science, National University of Defence Technology - China
  • LEI SUN College of Science, National University of Defence Technology - China
  • JIANSHU LUO College of Science, National University of Defence Technology - China

DOI:

https://doi.org/10.5380/bcg.v19i4.34878

Keywords:

Geodésia

Abstract

Noise estimation of hyperspectral remote sensing image is important for its
post-processing and application. In this paper, not only the spectral correlation
removing is considered, but the spatial correlation removing by wavelet transform is
considered as well. Therefore, a new method based on multiple linear regression
(MLR) and wavelet transform is proposed to estimate the noise of hyperspectral
remote sensing image. Numerical simulation of AVIRIS data is carried out and the
real data Hyperion is also used to validate the proposed algorithm. Experimental
results show that the method is more adaptive and accurate than the general MLR
and the other classified methods.

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Published

2013-12-20

How to Cite

XU, D., SUN, L., & LUO, J. (2013). NOISE ESTIMATION OF HYPERSPECTRAL REMOTE SENSING IMAGE BASED ON MULTIPLE LINEAR REGRESSION AND WAVELET TRANSFORM. Bulletin of Geodetic Sciences, 19(4). https://doi.org/10.5380/bcg.v19i4.34878

Issue

Vol. 19 No. 4 (2013)

Section

Article

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