NOISE ESTIMATION OF HYPERSPECTRAL REMOTE SENSING IMAGE BASED ON MULTIPLE LINEAR REGRESSION AND WAVELET TRANSFORM - XU, DONG; SUN, LEI; LUO, JIANSHU (Boletim de Ciências Geodésicas, 2013)
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.