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Study on sea ice classification of HY-1C satellite coastal zone imager images based on the optimal feature set
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Jinxia Zang1, Jianqiang Liu2, *, Xiaobin Yin1, Tao Zeng2, Lei Zhou1
Haiyang Xuebao | 2022, 44(5) : 35 - 46
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Haiyang Xuebao | 2022, 44(5): 35-46
• Haiyang-1C/D Satellite Data Processing and Typical Applications •
Study on sea ice classification of HY-1C satellite coastal zone imager images based on the optimal feature set
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Jinxia Zang1, Jianqiang Liu2, *, Xiaobin Yin1, Tao Zeng2, Lei Zhou1
Affiliations
  • 1. PIESAT Information Technology Co., Ltd., Beijing 100195, China
  • 2. National Satellite Ocean Application Service, Beijing 100081, China
Published: 2022-05-01 doi: 10.12284/hyxb2022021
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A support vector machine (SVM) sea ice classification method of Haiyang-1C (HY-1C) satellite coastal zone imager (CZI) images based on the optimal feature set is proposed in this paper. The spectral features and the texture features of CZI images are extracted, and then distance separability criterion is used for feature selection to obtain the optimal feature set. The sea ice classification experiment and analysis of the three CZI images of Liaodong Bay are carried out based on SVM classification method with the optimal feature set as the input of the classifier. The results show that the sea ice classification accuracy obtained by the proposed method is better than that of only using the spectral features or the texture features. The sea ice classification accuracy of December 19, 2020, January 10, 2021 and January 16, 2021 are 93.67%, 91.75% and 84.89%, respectively, all above 80%. The sea ice area of Liaodong Bay is estimated according to the sea ice classification map. It is found that the sea ice area of Liaodong Bay in the three images increased successively, and the maximum area is about 11 998.98 km2.

Haiyang-1C satellite  /  spectral features  /  texture features  /  optimal feature set  /  sea ice classification
Jinxia Zang, Jianqiang Liu, Xiaobin Yin, Tao Zeng, Lei Zhou. Study on sea ice classification of HY-1C satellite coastal zone imager images based on the optimal feature set[J]. Haiyang Xuebao, 2022 , 44 (5) : 35 -46 . DOI: 10.12284/hyxb2022021
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Year 2022 volume 44 Issue 5
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Article Info
doi: 10.12284/hyxb2022021
  • Receive Date：2021-03-03
  • Online Date：2026-02-01
  • Published：2022-05-01
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  • Received：2021-03-03
  • Revised：2021-07-30
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Affiliations
    1. PIESAT Information Technology Co., Ltd., Beijing 100195, China
    2. National Satellite Ocean Application Service, 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
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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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