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