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Application of convolutional neural networks in satellite remote sensing sea ice image classification: A case study of sea ice in the Bohai Sea
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Yanrong Cui1, 2, Bin Zou1, 2, 3, *, Zhen Han1, Lijian Shi2, 3, Sen Liu2
Haiyang Xuebao | 2020, 42(9) : 100 - 109
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Haiyang Xuebao | 2020, 42(9): 100-109
• Marine Technology •
Application of convolutional neural networks in satellite remote sensing sea ice image classification: A case study of sea ice in the Bohai Sea
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Yanrong Cui1, 2, Bin Zou1, 2, 3, *, Zhen Han1, Lijian Shi2, 3, Sen Liu2
Affiliations
  • 1 College of Marine Science, Shanghai Ocean University, Shanghai 201306, China
  • 2 National Satellite Ocean Application Service, Beijing 100081, China
  • 3 Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China
Published: 2020-09-25 doi: 10.3969/j.issn.0253-4193.2020.09.011
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This paper constructs a convolutional neural network based on TensorFlow. According to the idea of migration learning, the classical handwritten digit recognition is introduced as an introduction. The influence of different cost functions and activation function combinations on the classification results of convolutional neural network models is evaluated. Taking HJ-1A/B sea ice images as experimental data source, we analysis the influence of different function combinations on remote sensing sea ice image classification. It turns out that the cross-entropy cost function and the ReLU activation function are optimally combined. The feasibility of CNN in remote sensing sea ice classification is proved, and the classification results of the sea ice images in the Bohai Sea are verified. The calibration accuracy of the labeled samples is 98.4%. The model is then used to identify the unlabeled test samples. The influence of the window size on the sea ice classification results is discussed, and the optimal window size is 2×2 in the 400×400 small-scale classification experiment. Finally, the identification and verification of the entire Bohai Sea area is carried out, and the effect is good.

CNN  /  sea ice classification  /  cost function  /  activation function  /  TensorBoard
Yanrong Cui, Bin Zou, Zhen Han, Lijian Shi, Sen Liu. Application of convolutional neural networks in satellite remote sensing sea ice image classification: A case study of sea ice in the Bohai Sea[J]. Haiyang Xuebao, 2020 , 42 (9) : 100 -109 . DOI: 10.3969/j.issn.0253-4193.2020.09.011
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Article Info
doi: 10.3969/j.issn.0253-4193.2020.09.011
  • Receive Date：2019-07-03
  • Online Date：2026-03-27
  • Published：2020-09-25
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  • Received：2019-07-03
  • Revised：2020-01-07
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Affiliations
    1 College of Marine Science, Shanghai Ocean University, Shanghai 201306, China
    2 National Satellite Ocean Application Service, Beijing 100081, China
    3 Key Laboratory of Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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