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Research on cloud detection for HY-1C CZI remote sensing images collected over lands

  • role： First author第一作者
  • Affiliation：

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

  • Email：binyang@hnu.edu.cn
  • Introduction：杨彬， 研究方向为遥感图像处理。E-mail： binyang@hnu.edu.cn
YANG Bin1,  
  • Affiliation：

    College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

GUO Jinyuan1,  
  • Affiliation：

    College of Mechanical Engineering, University of South China, Hengyang 421001, China

HE Peng2,  
  • Affiliation：

    National Satellite Ocean Application Service, Beijing 100081, China

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

YE Xiaomin34,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Satellite Ocean Application Service, Beijing 100081, China

    Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

  • Email：jqliu@mail.nsoas.org.cn
  • Introduction：刘建强， 研究方向为海洋卫星数据分析。E-mail： jqliu@mail.nsoas.org.cn
LIU Jianqiang34*

Resümee

The Coast Zone Imager (CZI) onboard the Chinese first marine aqua-color satellite HY-1C started operational operations in June 2019. The data acquired by CZI have the characteristics of medium resolution, large width and high revisit period and taking into account the requirements of ocean water color, terrestrial ecology and polar glaciers. Therefore, the large amount of coastal, land, and ocean data acquired by CZI is of great significance for marine disaster and environmental monitoring research. However, related studies have shown that clouds cover an average of 68% of the earth's surface. CZI data is severely affected by cloud, which will then have a strong impact on its subsequent applications. The effective identification of clouds in remote sensing images is extremely important for the application of CZI images. Most of the existing cloud detection algorithms are based on RGB images or multi-spectral images including thermal infrared band. There are few researches on cloud detection algorithms for RGB-NIR four-band remote sensing images, such as HY-1C CZI. The objective of this paper is thus to propose an unsupervised cloud detection method for HY-1C CZI remote sensing images that makes full use of NIR band information. The method includes four processes: training samples selection, feature extraction, Support Vector Machine (SVM) classification, and post-processing. In the selection of training samples, combining dark channel reflectivity, normalized vegetation index and whiteness index of the image, this paper proposes an automatic training sample extraction algorithm, which uses the whiteness index to obtain detail information, and accurately extract cloud/non-cloud samples through a gradual refinement process. For feature extraction, the spatial spectrum feature information of CZI remote sensing image is selected, including reflectance, spectral index, texture and structure features, to characterize remote sensing image features, and maximize the feature difference between cloud and non-cloud regions. Based on the above automatically extracted sample and its feature description, SVM is used to initially classify the CZI remote sensing data, and then the guided filtering, hole filling and geometric judgment post-processing are performed to obtain the final high-precision cloud detection results. This paper applies the algorithm to four typical scenarios (vegetation, soil, wetland, and ice and snow scenarios), and compares and analyzes it with the currently popular unsupervised cloud detection algorithms. Compared with other cloud detection algorithms, the qualitative analysis results show that the cloud detection results in this paper are in good agreement with real cloud distribution image labeled by human. In addition, the most commonly used error rate metric is also used to quantitatively evaluate the cloud detection results. It shows that the error rates of the proposed algorithm in vegetation, soil, wetland, ice and snow scenes are 0.027, 0.064, 0.026, and 0.049, respectively; and that has the lowest error rate in four scenarios. Through the above comparative analysis, the detection results of the proposed algorithm in different scenarios are more accurate, which demonstrates the effectiveness of the proposed algorithm for cloud detection from HY-1C CZI data.

Schlüsselwort

HY-1C;Coast Zone Imager (CZI);cloud detection;Whiteness Index;unsupervised

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