A two-branch cloud detection algorithm based on the fusion of a feature enhancement module and Gaussian mixture model

Fangrong Zhou; Gang Wen; Yi Ma; Yutang Ma; Hao Pan; Hao Geng; Jun Cao; Yitong Fu; Shunzhen Zhou; Kaizheng Wang; Fangrong Zhou; Gang Wen; Yi Ma; Yutang Ma; Hao Pan; Hao Geng; Jun Cao; Yitong Fu; Shunzhen Zhou; Kaizheng Wang

doi:10.3934/mbe.2023955

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 12: 21588-21610. doi: 10.3934/mbe.2023955

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A two-branch cloud detection algorithm based on the fusion of a feature enhancement module and Gaussian mixture model

1.
Joint Laboratory of Power Remote Sensing Technology (Electric Power Research Institute, Yunnan Power Grid Company ltd.), Kunming 650217, China
2.
Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650000, China

Academic Editor: Guanqiu Qi

Received: 11 October 2023 Revised: 28 November 2023 Accepted: 28 November 2023 Published: 05 December 2023

Accurate cloud detection is an important step to improve the utilization rate of remote sensing (RS). However, existing cloud detection algorithms have difficulty in identifying edge clouds and broken clouds. Therefore, based on the channel data of the Himawari-8 satellite, this work proposes a method that combines the feature enhancement module with the Gaussian mixture model (GMM). First, statistical analysis using the probability density functions (PDFs) of spectral data from clouds and underlying surface pixels was conducted, selecting cluster features suitable for daytime and nighttime. Then, in this work, the Laplacian operator is introduced to enhance the spectral features of cloud edges and broken clouds. Additionally, enhanced spectral features are input into the debugged GMM model for cloud detection. Validation against visual interpretation shows promising consistency, with the proposed algorithm outperforming other methods such as RF, KNN and GMM in accuracy metrics, demonstrating its potential for high-precision cloud detection in RS images.
- cloud detection,
- Himawari-8,
- Gaussian mixture model,
- probability density function,
- Laplacian operator
Citation: Fangrong Zhou, Gang Wen, Yi Ma, Yutang Ma, Hao Pan, Hao Geng, Jun Cao, Yitong Fu, Shunzhen Zhou, Kaizheng Wang. A two-branch cloud detection algorithm based on the fusion of a feature enhancement module and Gaussian mixture model[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21588-21610. doi: 10.3934/mbe.2023955

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Abstract

Accurate cloud detection is an important step to improve the utilization rate of remote sensing (RS). However, existing cloud detection algorithms have difficulty in identifying edge clouds and broken clouds. Therefore, based on the channel data of the Himawari-8 satellite, this work proposes a method that combines the feature enhancement module with the Gaussian mixture model (GMM). First, statistical analysis using the probability density functions (PDFs) of spectral data from clouds and underlying surface pixels was conducted, selecting cluster features suitable for daytime and nighttime. Then, in this work, the Laplacian operator is introduced to enhance the spectral features of cloud edges and broken clouds. Additionally, enhanced spectral features are input into the debugged GMM model for cloud detection. Validation against visual interpretation shows promising consistency, with the proposed algorithm outperforming other methods such as RF, KNN and GMM in accuracy metrics, demonstrating its potential for high-precision cloud detection in RS images.

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