Classification accuracy and influencing factors of Arctic sea ice based on deep learning and Sentinel-1 satellite imagery

doi:10.3969/j.issn.1001-909X.2024.03.010

Abstract

Abstract:

The type of sea ice is one of the important attributes of polar sea ice, and the physical properties of multi-year ice are significantly different from those of first-year ice. Therefore, the identifying the types of sea ice is of great significance to the research of polar climate change and the navigation security of ships in ice-covered regions. Satellite remote sensing is an effective method to obtain multi-temporal and large-scale sea ice type information. Based on three deep learning models (ResNet, Vision Transformer, Swin Transformer) and Sentinel-1 satellite dual-polarization synthetic aperture radar (SAR) images, this paper studies the classification method for sea ice in the regions of the Northwest and Northeast Passage in the Arctic. The results showed that the sea ice classification effect of 8×8 pixel slice dataset was better than that of other size slice datasets. Offset processing false color images could effectively reduce the influence of noise on sea ice classification. Among the three deep learning models, the Swin Transformer model had the highest classification accuracy, with the overall accuracy and Kappa coefficient above 98%. Comparing the multi-year ice concentration, it was found that the results of the three models deviate less than 10% from the AMSR2 data.

Key words: artificial intelligence(AI), neural network, Swin Transformer, sea ice classification, multi-year ice, Arctic Passage, synthetic aperture radar(SAR)

CLC Number:

P731.15
P714

SHAO Zhiyuan, ZHAO Jiechen, XIE Longxiang, MU Fangru, XIAO Jing, LIU Minjun, CHEN Xuejing. Classification accuracy and influencing factors of Arctic sea ice based on deep learning and Sentinel-1 satellite imagery[J]. Journal of Marine Sciences, 2024, 42(3): 119-130.

Figures/Tables 10

References 37

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序号	成像日期	经度	纬度	卫星	用途
1	2021-10-27	131.45°W—111.54°W	73.83°N —78.49°N	Sentinel-1B	测试
2	2022-01-30	111.25°W—95.77°W	70.29°N —74.80°N	Sentinel-1A	测试
3	2022-01-31	72.80°W—58.44°W	68.99°N —73.47°N	Sentinel-1A	测试
4	2022-10-31	134.51°W—107.33°W	76.85°N —81.92°N	Sentinel-1A	训练
5	2022-10-31	140.17°W—124.76°W	69.97°N —74.69°N	Sentinel-1A	训练
6	2022-11-24	113.12°W—93.62°W	73.58°N —78.22°N	Sentinel-1A	训练
7	2022-11-26	111.20°W—95.77°W	70.29°N —74.80°N	Sentinel-1A	训练
8	2022-11-27	96.81°W—81.42°W	70.25°N —74.76°N	Sentinel-1A	训练
9	2023-01-18	86.97°W—68.44°W	72.78°N —77.41°N	Sentinel-1A	测试
10	2021-12-09	77.70°E—99.03°E	77.24°N —82.11°N	Sentinel-1B	测试
11	2022-01-16	59.76°E—79.41°E	73.68°N —78.33°N	Sentinel-1A	测试
12	2022-01-27	112.31°E—139.43°E	76.82°N —81.90°N	Sentinel-1A	训练
13	2022-01-27	109.03°E—128.43°E	73.55°N —78.19°N	Sentinel-1A	训练
14	2022-11-29	81.86°E—101.92°E	73.00°N —78.31°N	Sentinel-1A	训练
15	2022-12-10	39.32°E—52.06°E	66.83°N —71.26°N	Sentinel-1A	训练
16	2023-02-04	87.81°E—115.34°E	76.98°N —82.06°N	Sentinel-1A	测试
17	2023-02-20	131.25°E—151.36°E	73.03°N —78.34°N	Sentinel-1A	训练
18	2023-03-08	49.27°E—64.70°E	72.78°N —77.41°N	Sentinel-1A	测试

序号	成像日期	经度	纬度	卫星	用途
1	2021-10-27	131.45°W—111.54°W	73.83°N —78.49°N	Sentinel-1B	测试
2	2022-01-30	111.25°W—95.77°W	70.29°N —74.80°N	Sentinel-1A	测试
3	2022-01-31	72.80°W—58.44°W	68.99°N —73.47°N	Sentinel-1A	测试
4	2022-10-31	134.51°W—107.33°W	76.85°N —81.92°N	Sentinel-1A	训练
5	2022-10-31	140.17°W—124.76°W	69.97°N —74.69°N	Sentinel-1A	训练
6	2022-11-24	113.12°W—93.62°W	73.58°N —78.22°N	Sentinel-1A	训练
7	2022-11-26	111.20°W—95.77°W	70.29°N —74.80°N	Sentinel-1A	训练
8	2022-11-27	96.81°W—81.42°W	70.25°N —74.76°N	Sentinel-1A	训练
9	2023-01-18	86.97°W—68.44°W	72.78°N —77.41°N	Sentinel-1A	测试
10	2021-12-09	77.70°E—99.03°E	77.24°N —82.11°N	Sentinel-1B	测试
11	2022-01-16	59.76°E—79.41°E	73.68°N —78.33°N	Sentinel-1A	测试
12	2022-01-27	112.31°E—139.43°E	76.82°N —81.90°N	Sentinel-1A	训练
13	2022-01-27	109.03°E—128.43°E	73.55°N —78.19°N	Sentinel-1A	训练
14	2022-11-29	81.86°E—101.92°E	73.00°N —78.31°N	Sentinel-1A	训练
15	2022-12-10	39.32°E—52.06°E	66.83°N —71.26°N	Sentinel-1A	训练
16	2023-02-04	87.81°E—115.34°E	76.98°N —82.06°N	Sentinel-1A	测试
17	2023-02-20	131.25°E—151.36°E	73.03°N —78.34°N	Sentinel-1A	训练
18	2023-03-08	49.27°E—64.70°E	72.78°N —77.41°N	Sentinel-1A	测试

区域	切片大小	准确率/%			整体准确率/%	Kappa 系数/%
区域	切片大小	海水	一年冰	多年冰	整体准确率/%	Kappa 系数/%
2号	32×32像素	100.00	100.00	98.88	99.62	99.44
	16×16像素	100.00	100.00	98.88	99.62	99.44
	8×8像素	99.25	100.00	99.88	99.71	99.63
16号	32×32像素	100.00	100.00	97.50	99.17	98.75
	16×16像素	100.00	99.63	99.38	99.67	99.50
	8×8像素	100.00	99.88	100.00	99.96	99.94

区域	切片大小	准确率/%			整体准确率/%	Kappa 系数/%
区域	切片大小	海水	一年冰	多年冰	整体准确率/%	Kappa 系数/%
2号	32×32像素	100.00	100.00	98.88	99.62	99.44
	16×16像素	100.00	100.00	98.88	99.62	99.44
	8×8像素	99.25	100.00	99.88	99.71	99.63
16号	32×32像素	100.00	100.00	97.50	99.17	98.75
	16×16像素	100.00	99.63	99.38	99.67	99.50
	8×8像素	100.00	99.88	100.00	99.96	99.94

区域	偏移量处理情况	准确率/%			整体准确率/%	Kappa 系数/%
区域	偏移量处理情况	海水	一年冰	多年冰	整体准确率/%	Kappa 系数/%
3号	未处理	99.88	99.63	77.63	92.37	88.56
3号	处理后	99.25	100.00	100.00	99.83	99.75
10号	未处理	99.75	95.75	96.38	97.29	95.94
10号	处理后	99.25	100.00	100.00	99.75	99.63