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A fine classification method for sea ice based on random forest combining texture feature and NDVI
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Zhiyong Wang1, Mengyue Zhang1, *, Yaran Yu1, Ping Ni1
Haiyang Xuebao | 2021, 43(10) : 149 - 156
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Haiyang Xuebao | 2021, 43(10): 149-156
Article
A fine classification method for sea ice based on random forest combining texture feature and NDVI
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Zhiyong Wang1, Mengyue Zhang1, *, Yaran Yu1, Ping Ni1
Affiliations
  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Published: 2021-10-25 doi: 10.12284/hyxb2021167
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The accurate classification of sea ice is of great significance for mastering the growth and development of sea ice and ensuring the safety of navigation. Due to the influence of data sources and classification methods, the improvement of sea ice classification accuracy is limited. In this paper, for high spatial resolution optical remote sensing images, an accurate sea ice classification method based on texture features and normalized difference vegetation index (NDVI) was proposed, and a random forest classifier was used to construct a sea ice classification method. Taking Jiaozhou Bay of Qingdao as the experimental area and GF-2 as the experimental data, the sea ice types were extracted and compared with other classification methods. The results show that for GF-2 high-resolution optical remote sensing data, compared with the traditional random forest, support vector machine, automatic classification and regression tree methods and maximum likelihood classification method of combining texture features, the overall classification accuracy was improved by 13.70%, 11.60%, 19.22% and 29.37%, respectively. The Kappa coefficient was increased by 0.16, 0.13, 0.22 and 0.44, respectively. Compared with the random forest method based on texture features and normalized difference water index, the overall classification accuracy was improved by 9.67% and Kappa coefficient was increased by 0.09. It shows that the sea ice classification method constructed in this paper can effectively improve the accuracy of sea ice classification, and provide an effective technical means for the accurate classification of sea ice.

sea ice classification  /  GF-2 image  /  random forest  /  texture feature  /  NDVI
Zhiyong Wang, Mengyue Zhang, Yaran Yu, Ping Ni. A fine classification method for sea ice based on random forest combining texture feature and NDVI[J]. Haiyang Xuebao, 2021 , 43 (10) : 149 -156 . DOI: 10.12284/hyxb2021167
Year 2021 volume 43 Issue 10
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doi: 10.12284/hyxb2021167
  • Receive Date:2020-09-01
  • Online Date:2026-02-26
  • Published:2021-10-25
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  • Received:2020-09-01
  • Revised:2021-04-15
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    1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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