Multi-channel satellite cloud image fusion in the tetrolet transform domain

Zhang, Changjiang; Chen, Yuan; Duanmu, Chunjiang; Feng, Huanqing

doi:10.1080/01431161.2014.980918

Cited by 5 publications

(7 citation statements)

References 19 publications

(23 reference statements)

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“…If these details are neglected, the fused results always lost a lot of targets' contours. Zhang et al proposed a Laplacian pyramid for decomposing the low-frequency portion of the TT and proved that the Laplacian pyramid is conducive to improve the capability of describing details [18]. The result shows that the proposed algorithm performs well when fusing multichannel satellite cloud images.…”

Section: Introductionmentioning

confidence: 73%

A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

Chen

et al. 2018

Mathematical Problems in Engineering

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An algorithm based on pulse-coupled neural network (PCNN) constructed in the Tetrolet transform domain is proposed for the fusion of the visible and passive millimeter wave images in order to effectively identify concealed targets. The Tetrolet transform is applied to build the framework of the multiscale decomposition due to its high sparse degree. Meanwhile, a Laplacian pyramid is used to decompose the low-pass band of the Tetrolet transform for improving the approximation performance. In addition, the maximum criterion based on regional average gradient is applied to fuse the top layers along with selecting the maximum absolute values of the other layers. Furthermore, an improved PCNN model is employed to enhance the contour feature of the hidden targets and obtain the fusion results of the high-pass band based on the firing time. Finally, the inverse transform of Tetrolet is exploited to obtain the fused results. Some objective evaluation indexes, such as information entropy, mutual information, and / , are adopted for evaluating the quality of the fused images. The experimental results show that the proposed algorithm is superior to other image fusion algorithms.

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Section: Introductionmentioning

confidence: 73%

A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

Chen

et al. 2018

Mathematical Problems in Engineering

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“…In the field of meteorology, to obtain more accurate cloud coverage information, multi-source data fusion is usually performed based on spectral bands and scale geometry information of instantaneous satellite images. Examples include various transforms including the contourlet (Miao and Wang, 2006;Jin et al, 2011), curvelet (Li and Yang, 2008;Liu et al, 2015), NSCT (Wang et al, 2012), and tetrolet transforms (Zhang et al, 2014). Alternatively, based on the field of view of different observation instruments used to acquire satellite images and of ground-based stations, methods such as the stepwise revision method (Kenyon et al, 2016) and data assimilation technology (Hu and Xue, 2007) have been used.…”

Section: Introductionmentioning

confidence: 99%

“…Two main types of methods exist for merging multiple satellite thematic products based on the principle of calculation. One type of fusing approach provides spatiotemporal data fusion by spectral correlation, which is more suitable for the regions where the spatial information of objects has no obvious change, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the improved STARFM (Gao et al, 2006;Hilker et al, 2009;Zhu et al, 2010;Zhang et al, 2014). The other type of spatiotemporal data fusing method is data-driven, which involves developing geostatistical models to solve the problem created when the same parameter is inconsistent among different satellite products.…”

Section: Introductionmentioning

confidence: 99%

A monthly 1-degree resolution dataset of cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

Liu

Liang

et al. 2023

Preprint

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Abstract. The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1°×1° CF dataset in the Arctic during 2000–2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10–20 % after fusing. Compared with ground-based observations, R2 increased by about 0.20–0.48 and the root mean square error (RMSE) and bias reductions averaged about 6.09 % and 4.04 % for land regions, respectively; these metrics for ocean regions were about 0.05–0.31, 2.85 %, and 3.15 %, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77 % and 4.31 %, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2, 4.46 % for RMSE and, 3.92 % for bias. The comparison with the ERA5 reanalysis and CMIP6 CF datasets shows that the proposed fusion algorithm effectively corrected the CF data with differences greater than 30 %. Moreover, the fused product effectively supplements the temporal gaps of AVHRR-based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5° N. A continuous monthly 1-degree CF product covering the entire Arctic during 2000–2020 was generated and is freely available to the public at https://doi.org/10.5281/zenodo.7624605 (Liu et al., 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the earth’s energy imbalance.

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“…In the field of meteorology, to obtain more accurate cloud coverage information, multi-source data fusion is usually performed based on spectral bands and scale geometry information of instantaneous satellite images. Examples include various transforms including the contourlet (Miao and Wang, 2006;Jin et al, 2011), curvelet (Li and Yang, 2008;Liu et al, 2015), NonSubsampled Contourlet Transform (NSCT) (Wang et al, 2012), and tetrolet transforms (Zhang et al, 2014). Alternatively, based on the field of view of different observation instruments used to acquire satellite images and of ground-based stations, methods such as the stepwise revision method (Kenyon et al, 2016) and data assimilation technology (Hu and Xue, 2007) have been used.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

Liu

Liang

et al. 2023

Earth Syst. Sci. Data

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Abstract. The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatiotemporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1∘ × 1∘ CF dataset in the Arctic during 2000–2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10 %–20 % after fusing, with particularly noticeable improvements in the vicinity of Greenland. Compared with ground-based observations, R2 increased by about 0.20–0.48, and the root mean square error (RMSE) and bias reductions averaged about 6.09 % and 4.04 % for land regions, respectively; these metrics for ocean regions were about 0.05–0.31, 2.85 %, and 3.15 %, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77 % and 4.31 %, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2, 4.46 % for RMSE, and 3.92 % for bias. The results of the comparison with ERA5 and the Meteorological Research Institute – Atmospheric General Circulation model version 3.2S (MRI-AGCM3-2-S) climate model suggest an obvious improvement in the consistency between the satellite-observed CF and the reanalysis and model data after fusion. This serves as a promising indication that the fused CF results hold the potential to deliver reliable satellite observations for modeling and reanalysis data. Moreover, the fused product effectively supplements the temporal gaps of Advanced Very High Resolution Radiometer (AVHRR)-based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and it extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5∘ N. A continuous monthly 1∘ CF product covering the entire Arctic during 2000–2020 was generated and is freely available to the public at https://doi.org/10.5281/zenodo.7624605 (Liu and He, 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the Earth's energy imbalance.

show abstract

Multi-channel satellite cloud image fusion in the tetrolet transform domain

Cited by 5 publications

References 19 publications

A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

A Visible and Passive Millimeter Wave Image Fusion Algorithm Based on Pulse-Coupled Neural Network in Tetrolet Domain for Early Risk Warning

A monthly 1-degree resolution dataset of cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

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