سابِق|تال

Detection of severe convective cloud over sea surface from geostationary meteorological satellite images based on deep learning

  • role： First author第一作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：zhengyq@radi.ac.cn
  • Introduction：郑益勤,1993年,男,硕士研究生, 研究方向为海洋遥感、深度学习。E-mail: zhengyq@radi.ac.cn
ZHENG Yiqin12,  
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

YANG Xiaofeng1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：lizw@radi.ac.cn
  • Introduction：李紫薇，1960年,女,研究员, 研究方向为海洋遥感。E-mail: lizw@radi.ac.cn
LI Ziwei1*

ملخص

Disastrous weather caused by Severe Convective Clouds (SCC), such as short-term heavy rainfall, hail, thunderstorms, squall line and tornado, has a serious impact on the life and production over sea surface. Therefore it is very essential to monitor SCC duly and accurately. However, the traditional methods are difficult to meet the requirements due to the short life cycle of severe convective weather and the lack of meteorological stations over sea surface. The geostationary meteorological satellite images have wide coverage area and high temporal resolution, so it has become an important means to monitor severe convective weather. With the rapid development of deep learning, it has been applied in the field of remote sensing image recognition.This paper proposes a method for automatic identification of SCC from geostationary meteorological satellite images based on Deep Belief Networks (DBN). First, in order to identify the characteristic parameters that can be used for recognizing severe convective clouds, the severe convective clouds are analyzed from spectral features and texture features. Second, building automatic recognition algorithm based on deep belief networks which consists of multiple Restricted Boltzmann Machines (RBM) and a softmax classifier. And it is divided into two stages: unsupervised pre training and supervised fine tuning. This method is summarized in the following four steps: (1)data preprocessing: images splicing and region cutting; (2)extracting features and constructing sample sets: extracting the spectral features: TBB13, TBB08-TBB13 and TBB13-TBB15. And extracting the texture features: Energy and Contrast, which extracted based on spectral feature TBB08-TBB13. And then constructing the sample sets automatically referring to CloudSat satellite cloud classification products;(3)training DBN model: determining the structure and parameters of the model, including parameters of RBM and depth of DBN;(4)recognizing SCC using DBN model, and doing the postprocessing: identifying the severe convective clouds using DBN model, and processing the recognition results by category merging, closing operation and edge detection.The Himawari-8 satellite image data and CloudSat cloud classification products from March to May in 2017 were used in the experiment. The research area is 70°E—150°E, 0°N—55°N. After training, the structure of DBN model was setting to 245-140-140-140-135-135-135-9. The accuracy of the DBN model is evaluated with the modified test sample, the Critical Success Index (CSI) is 71.28%, the Probability of Detection (POD) is 84.83%, and the False Alarm Ratio (FAR) is 18.31%. Compared with single band threshold method, multi band threshold method and SVM, the method proposed in this paper can effectively improve the recognition accuracy.The experiment results show that all kinds of severe convective clouds in different phases from initiation to dissipation can be effectively identified. Consequently, it has the advantages of finding severe convective weather in advance. The majority of the cirrus can be removed, but the results of recognition still contain some cirrus spissatus, and the recognition of the cloud edge is not accurate enough, which will be two directions for future research.

مفهوم

remote sensing;severe convective clouds;deep belief networks;geostationary meteorological satellite;spectral feature;texture feature

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