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Extraction of Artemia slicks from HY-1C CZI images： Taking Ebinur Lake as an example

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

  • Email：yelanchuimeng@163.com
  • Introduction：王欣，研究方向为水环境定量遥感。E-mail： yelanchuimeng@163.com
WANG Xin12,  
  • Affiliation：

    National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China

LIU Jianqiang3,  
  • Affiliation：

    National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China

DING Jing3,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

TIAN Jingyi12,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

SUN Xianghan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

    Hubei LuoJia Laboratory, Wuhan 430079, China

  • Email：tianliqiao@whu.edu.cn
  • Introduction：田礼乔，研究方向为水体光学装备研发与组网观测、国产卫星水色遥感大数据处理与基于深度学习算法的水环境参数反演与遥感监测应用等。E-mail： tianliqiao@whu.edu.cn
TIAN Liqiao12*

Resümee

Artemia is a kind of small crustacean that lives in high salinity water, which can be used as an excellent fishery feed and an important component of carbon flux and biological chain in salt lakes. Because of its nonnegligible ecological and economic value, it is of great significance to develop a high-precision extraction method of Artemia based on remote sensing data for biological resource monitoring and reasonable fishing. Taking Ebinur Lake as an example, this paper proposed an automatic method to extract Artemia based on the HY-1C Coastal Zone Imager (CZI) images and deep learning technology. Firstly, the spectral characteristics of HY-1C CZI and Landsat-8 OLI sensors in the Artemia endmember were analyzed and the Spectral Band Adjustment Factors (SBAF) were used to eliminate the response differences between the two sensors to construct the Artemia-water dataset containing 837 effective samples of 64×64 size. Secondly, 70% of the dataset was used to train the U-Shaped Fully Convolutional Neural Network (U-Net) with a depth of 5, and the remaining 20% and 10% of the data were used to verify and test the algorithm, respectively. The model iterated 6700 times in the training process, which took 35 minutes. During this period, we used the adaptive moment estimation (Adam) optimizer with an initial learning rate of 1×10-4, and the binary cross entropy as the loss function. The training batch size was set to 4 since the equipment limitation. Whenever the loss value of the verification dataset did not decline within the last 3 epochs, the learning rate was halved. The training would be terminated automatically if it did not decline within the last 10 epochs. Finally, the impact factors and application potential of this method were further analyzed and discussed. The experimental results demonstrated that, compared with the Support Vector Machine (SVM), the Maximum likelihood Classification (MLC), and the Normalized Difference Water index (NDWI) algorithms, the extraction Precision and F1 score of U-Net were 92.02% and 90.55%, respectively, which were about 11%—23% higher than other methods. Even in the face of complex water background interference, the proposed method showed better robustness since the extraction error of the U-Net was only 3.3%. In addition, the maximum extraction area of Artemia slicks in 10 CZI images from 2019 to 2021 was 9.27 km2, 5.8 times larger than the minimum. So was the water area of Ebinur Lake, which varied sharply between 497.34 km2 and 330.93 km2. The violent difference in Artemia extraction may be due to the variations of water area and other natural or human factors such as temperature, wind speed, precipitation, pollution, and overfishing. It is necessary to conduct a profound study on their correlation relationship. Therefore, the future effort still needs to establish further a more representative and inclusive remote sensing dataset of Artemia, and develop more reliable and practical algorithms to carry out more long time series and large-scale studies in Artemia extraction.

Schlüsselwort

HY-1C Satellite;Coastal Zone Imager;Artemia slicks;Ebinur Lake;U-Net

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