TY - GEN
T1 - Local Spatial-Spectral Correlation Based Mixtures of Factor Analyzers for Hyperspectral Denoising
AU - Zhao, Bin
AU - Sveinsson, Johannes R.
AU - Ulfarsson, Magnus O.
AU - Chanussot, Jocelyn
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - This paper presents a local spatial-spectral correlation based mixtures of factor analyzers (LSSC-MFA) denoising method for hyperspectral image (HSI). HSIs are usually degraded by different noise types such as missing lines (ML), missing pixels (MP), salt and pepper noise (SP), and Gaussian noise. The proposed method, hierarchically, removes the mixed noise. Firstly, we develop a novel local spatial-spectral correlation (LSSC) method to remove the ML noise. Then LSSC-MFA uses the mixtures of factor analyzers (MFA) method to remove the MP, SP, and Gaussian noises. The performance of the proposed method has been validated using both real and simulated HSI datasets. Results on the simulated datasets confirm considerable improvements in terms of peak signal-to-noise ratio (PSNR) compared to the state-of-the-art denoising methods used in experiments. In addition, visual improvements can be observed in the case of real dataset experiments.
AB - This paper presents a local spatial-spectral correlation based mixtures of factor analyzers (LSSC-MFA) denoising method for hyperspectral image (HSI). HSIs are usually degraded by different noise types such as missing lines (ML), missing pixels (MP), salt and pepper noise (SP), and Gaussian noise. The proposed method, hierarchically, removes the mixed noise. Firstly, we develop a novel local spatial-spectral correlation (LSSC) method to remove the ML noise. Then LSSC-MFA uses the mixtures of factor analyzers (MFA) method to remove the MP, SP, and Gaussian noises. The performance of the proposed method has been validated using both real and simulated HSI datasets. Results on the simulated datasets confirm considerable improvements in terms of peak signal-to-noise ratio (PSNR) compared to the state-of-the-art denoising methods used in experiments. In addition, visual improvements can be observed in the case of real dataset experiments.
KW - Denoising
KW - hyperspectral image
KW - local spatial-spectral correlation based mixtures of factor analyzers
UR - https://www.scopus.com/pages/publications/85101992233
U2 - 10.1109/IGARSS39084.2020.9324038
DO - 10.1109/IGARSS39084.2020.9324038
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1488
EP - 1491
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -