Hyperspectral Image Denoising By Combining Ground Object Features with Low-Rank Characteristics

Dongmei Huang; Yonglan Li; Minghua Zhang; Wei Song

doi:10.3788/LOP57.121102

[1] Tong Q X, Zhang B, Zhang L F. Current progress of hyperspectral remote sensing in China[J]. Journal of Remote Sensing, 20, 689-707(2016).

[2] Cui R M. Hyperspectral image denoising and classification Xi'an:[D]. Xidian University, 17-19(2018).

[3] Liu L X, Li M Z, Zhao Z G et al. Recent advances of hyperspectral imaging application in biomedicine[J]. Chinese Journal of Lasers, 45, 0207017(2018).

[4] Skauli T. Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing[J]. Optics Express, 19, 13031-13046(2011).

[5] Sun L. Signal-dependent noise parameter estimation of hyperspectral remote sensing images[J]. Spectroscopy Letters, 48, 717-725(2015).

[6] Zhang B. Advancement of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 20, 1062-1090(2016).

[7] Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007).

[8] Zhang H Y, He W, Zhang L P et al. Hyperspectral image restoration using low-rank matrix recovery[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 4729-4743(2014).

[9] Wang M D, Yu J, Xue J H et al. Denoising of hyperspectral images using group low-rank representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 4420-4427(2016).

[10] Fan H X, Li J, Yuan Q Q et al. Hyperspectral image denoising with bilinear low rank matrix factorization[J]. Signal Processing, 163, 132-152(2019).

[11] Chen G Y, Qian S E. Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage[J]. IEEE Transactions on Geoscience and Remote Sensing, 49, 973-980(2011).

[12] Xue J Z, Zhao Y Q, Liao W Z et al. Joint spatial and spectral low-rank regularization for hyperspectral image denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 1940-1958(2018).

[13] Fan H Y, Chen Y J, Guo Y L et al. Hyperspectral image restoration using low-rank tensor recovery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 4589-4604(2017).

[14] Zhang X H, Hao R F, Li T Y. Hyperspectral abnormal target detection based on low rank and sparse matrix decomposition-sparse representation[J]. Laser & Optoelectronics Progress, 56, 042801(2019).

[15] Wang L G, Zhao C H. Hyperspectral image processing[M]. Heidelberg: Springer(2016).

[16] Rasti B, Scheunders P, Ghamisi P et al. Noise reduction in hyperspectral imagery: overview and application[J]. Remote Sensing, 10, 482(2018).

[17] He W, Zhang H Y, Zhang L P et al. Hyperspectral image denoising via noise-adjusted iterative low-rank matrix approximation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3050-3061(2015).

[18] He W, Zhang H Y, Zhang L P et al. Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 178-188(2016).

[19] FanF, MaY, LiC, et al. and low-rank representation[J]. InformationSciences, 2017, 397/398: 48- 68.

[20] Yin X, Ma J. Image fusion method based on entropy rate segmentation and multi-scale decomposition[J]. Laser & Optoelectronics Progress, 55, 011011(2018).

[21] Yao D. Hyperspectral image denoising and inpainting based on low-rank representation[D]. Beijing: University of Chinese Academy of Sciences(2018).

[22] Lin Z C, Liu R S. -10-18)[2019-03-01]. https:∥arxiv., org/abs/1009, 5055(2013).

[23] Wright J, Ganesh A, Rao S et al. Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization. [C]∥Proceedings of the 23rd Annual Conference on Neural Information Processing Systems, December 7-10, 2009, Vancouver, British Columbia, Canada. New York: Curran Associates, Inc, 1-9(2009).

[24] Zhou T, Tao D. GoDec: randomized lowrank & sparse matrix decomposition in noisy case. [C]∥International Conference on Machine Learning, ICML 2011, June 28 - July 2, 2011, Bellevue, Washington, USA. [S.l.: s.n.], 33-40(2011).

[25] Zhao Y Q, Yang J X, Zhang Q Y et al. Hyperspectral imagery super-resolution by sparse representation and spectral regularization[J]. EURASIP Journal on Advances in Signal Processing, 2011, 87(2011).