8.3.12.1 Thin Cloud Detection and Removal

Chapter Contents (Back)
Cloud Detection. When not quite a cloud:
See also Haze, Dehazing, Color Correction.

Shen, H.F.[Huan-Feng], Li, H.F.[Hui-Fang], Qian, Y.[Yan], Zhang, L.P.[Liang-Pei], Yuan, Q.Q.[Qiang-Qiang],
An effective thin cloud removal procedure for visible remote sensing images,
PandRS(96), No. 1, 2014, pp. 224-235.
Elsevier DOI 1410
Thin cloud removal BibRef

Shen, X.L.[Xiao-Le], Li, Q.Q.[Qing-Quan], Tian, Y.J.[Ying-Jie], Shen, L.L.[Lin-Lin],
An Uneven Illumination Correction Algorithm for Optical Remote Sensing Images Covered with Thin Clouds,
RS(7), No. 9, 2015, pp. 11848.
DOI Link 1511
BibRef

Shen, Y.[Yang], Wang, Y.[Yong], Lv, H.T.[Hai-Tao], Qian, J.[Jiang],
Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis,
RS(7), No. 9, 2015, pp. 11481.
DOI Link 1511
BibRef

Xu, M., Pickering, M., Plaza, A.J., Jia, X.,
Thin Cloud Removal Based on Signal Transmission Principles and Spectral Mixture Analysis,
GeoRS(54), No. 3, March 2016, pp. 1659-1669.
IEEE DOI 1603
Absorption BibRef

Wu, W.[Wei], Luo, J.C.[Jian-Cheng], Hu, X.D.[Xiao-Dong], Yang, H.P.[Hai-Ping], Yang, Y.P.[Ying-Pin],
A Thin-Cloud Mask Method for Remote Sensing Images Based on Sparse Dark Pixel Region Detection,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Lv, H., Wang, Y., Yang, Y.,
Modeling of Thin-Cloud TOA Reflectance Using Empirical Relationships and Two Landsat-8 Visible Band Data,
GeoRS(57), No. 2, February 2019, pp. 839-850.
IEEE DOI 1901
Cloud computing, Remote sensing, Optical sensors, Artificial satellites, Earth, Clouds, Optical imaging, observed and modeled top-of-atmosphere (TOA) reflectance from thin clouds BibRef

Xu, M.[Meng], Jia, X.P.[Xiu-Ping], Pickering, M.R.[Mark R.], Jia, S.[Sen],
Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform,
PandRS(149), 2019, pp. 215-225.
Elsevier DOI 1903
Cloud removal, Noise-adjusted principal components transform (NAPCT), Signal-to-noise ratio (S/N). BibRef

Li, W.B.[Wen-Bo], Li, Y.[Ying], Chen, D.[Di], Chan, J.C.W.[Jonathan Cheung-Wai],
Thin cloud removal with residual symmetrical concatenation network,
PandRS(153), 2019, pp. 137-150.
Elsevier DOI 1906
Convolutional neural network, Residual blocks, Symmetrical concatenation, Thin cloud removal BibRef

Guo, Q., Hu, H., Li, B.,
Haze and Thin Cloud Removal Using Elliptical Boundary Prior for Remote Sensing Image,
GeoRS(57), No. 11, November 2019, pp. 9124-9137.
IEEE DOI 1911
Remote sensing, Sensors, Correlation, Cloud computing, Estimation, Clouds, Earth, Elliptical boundary, haze thickness prior, visible remote sensing image dehazing BibRef

Li, J.[Jun], Wu, Z.C.[Zhao-Cong], Hu, Z.W.[Zhong-Wen], Zhang, J.Q.[Jia-Qi], Li, M.L.[Ming-Liang], Mo, L.[Lu], Molinier, M.[Matthieu],
Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,
PandRS(166), 2020, pp. 373-389.
Elsevier DOI 2007
Cloud removal, Thin clouds, Physical model of cloud distortion, Generative Adversarial Networks (GANs), Image decomposition BibRef

Wen, X.[Xue], Pan, Z.X.[Zong-Xu], Hu, Y.X.[Yu-Xin], Liu, J.Y.[Jia-Yin],
Generative Adversarial Learning in YUV Color Space for Thin Cloud Removal on Satellite Imagery,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Dong, P.Y.[Ping-Yi], Liu, L.[Lei], Li, S.L.[Shu-Lei], Hu, S.[Shuai], Bu, L.B.[Ling-Bing],
Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Li, J.[Jun], Wu, Z.C.[Zhao-Cong], Hu, Z.W.[Zhong-Wen], Li, Z.L.[Zi-Long], Wang, Y.S.[Yi-Song], Molinier, M.[Matthieu],
Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Chen, H.[Hui], Chen, R.[Rong], Li, N.N.[Nan-Nan],
Attentive generative adversarial network for removing thin cloud from a single remote sensing image,
IET-IPR(15), No. 4, 2021, pp. 856-867.
DOI Link 2106
BibRef

Xu, Z.X.[Zun-Xiao], Wu, K.[Kang], Wang, W.[Wuli], Lyu, X.R.[Xin-Rong], Ren, P.[Peng],
Semi-supervised thin cloud removal with mutually beneficial guides,
PandRS(192), 2022, pp. 327-343.
Elsevier DOI 2209
Thin cloud removal, Semi-supervised learning, Mutually beneficial guides BibRef

Wu, R.Z.[Ren-Zhe], Liu, G.X.[Guo-Xiang], Lv, J.[Jichao], Fu, Y.[Yin], Bao, X.[Xin], Shama, A.[Age], Cai, J.[Jialun], Sui, B.[Baikai], Wang, X.W.[Xiao-Wen], Zhang, R.[Rui],
An Innovative Approach for Effective Removal of Thin Clouds in Optical Images Using Convolutional Matting Model,
RS(15), No. 8, 2023, pp. 2119.
DOI Link 2305
BibRef

Zi, Y.[Yue], Ding, H.D.[Hai-Dong], Xie, F.Y.[Feng-Ying], Jiang, Z.G.[Zhi-Guo], Song, X.D.[Xue-Dong],
Wavelet Integrated Convolutional Neural Network for Thin Cloud Removal in Remote Sensing Images,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Guo, Y.J.[Yu-Jun], He, W.[Wei], Xia, Y.[Yu], Zhang, H.Y.[Hong-Yan],
Blind single-image-based thin cloud removal using a cloud perception integrated fast Fourier convolutional network,
PandRS(206), 2023, pp. 63-86.
Elsevier DOI 2312
Cloud removal, Single-image-based, Fast Fourier convolution (FFC), Cloud perception, Long-range modeling BibRef


Ding, H.D.[Hai-Dong], Zi, Y.[Yue], Xie, F.Y.[Feng-Ying],
Uncertainty-based Thin Cloud Removal Network via Conditional Variational Autoencoders,
ACCV22(III:52-68).
Springer DOI 2307
BibRef

Toizumi, T., Zini, S., Sagi, K., Kaneko, E., Tsukada, M., Schettini, R.,
Artifact-Free Thin Cloud Removal Using Gans,
ICIP19(3596-3600)
IEEE DOI 1910
Remote sensing, Generative adversarial nets, Multi-spectral image, Cloud removal. BibRef

Liu, X.[Xing], Jiang, S.M.[Song-Mei],
A new method of information enhancement for hyper-spectrum image covered with thin clouds and realization based on IDL,
IASP09(5-8).
IEEE DOI 0904
BibRef

Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Cloud Detection, Ground-Based .


Last update:Mar 25, 2024 at 16:07:51