基于深度学习和Sentinel-1卫星影像的北极海冰分类精度和影响因素

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

海洋学研究 ›› 2024, Vol. 42 ›› Issue (3): 119-130.DOI: 10.3969/j.issn.1001-909X.2024.03.010

基于深度学习和Sentinel-1卫星影像的北极海冰分类精度和影响因素

邵志远¹^,²^,³(), 赵杰臣¹^,²^,⁴^,^*(), 解龙翔¹^,²^,³, 牟芳如¹^,², 肖静¹^,², 刘敏君¹^,², 陈雪婧¹^,²

1.哈尔滨工程大学青岛创新发展基地,山东青岛 266000
2.青岛哈尔滨工程大学创新发展中心,山东青岛 266000
3.宁波上航测绘股份有限公司,浙江宁波 315200
4.哈尔滨工程大学极地海洋声学与技术应用教育部重点实验室,黑龙江哈尔滨 150001

收稿日期:2023-10-08 修回日期:2024-04-17 出版日期:2024-09-15 发布日期:2024-11-25
通讯作者: ^*赵杰臣(1984—),男,副教授,主要从事极地海冰观测和数值预报研究,E-mail:zhaojiechen@hrbeu.edu.cn。
作者简介:邵志远(1998—),男,江苏省灌南县人,主要从事极地遥感研究,E-mail:shaozhiyuan@hrbeu.edu.cn。
基金资助:
哈尔滨工程大学青年科学家培育基金项目(79000012/006);山东省泰山学者工程资助项目(2023)

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

SHAO Zhiyuan¹^,²^,³(), ZHAO Jiechen¹^,²^,⁴^,^*(), XIE Longxiang¹^,²^,³, MU Fangru¹^,², XIAO Jing¹^,², LIU Minjun¹^,², CHEN Xuejing¹^,²

1. Qingdao Innovation and Development Base of Harbin Engineering University, Qingdao 266000, China
2. Qingdao Innovation and Development Center of Harbin Engineering University, Qingdao 266000, China
3. Ningbo Shanghang Surveying and Mapping Co. Ltd., Ningbo 315200, China
4. Key Laboratory for Polar Acoustics and Application of Ministry of Education of Harbin Engineering University, Harbin 150001, China

Received:2023-10-08 Revised:2024-04-17 Online:2024-09-15 Published:2024-11-25

摘要/Abstract

摘要：

海冰类型是极地海冰的重要属性之一,多年冰的物理性质较一年冰有着显著差异,因此识别海冰类型对极地气候变化研究和冰区船舶航行保障意义重大。卫星遥感是获取多时序、大范围海冰信息的有效手段。该文以北极西北航道和东北航道为研究区域,基于3个深度学习模型(ResNet、Vision Transformer、Swin Transformer)对Sentinel-1卫星双极化合成孔径雷达影像进行海冰分类研究。结果表明,8×8像素切片数据集的海冰分类效果优于其他尺寸切片数据集;对假彩色合成图像进行偏移量处理能够有效地减少噪声对海冰分类的影响;在3个深度学习模型中,Swin Transformer模型分类精度最高,整体准确率和Kappa系数均在98%以上。比较多年冰密集度数据发现,3个模型的结果与AMSR2的偏差均小于10%。

关键词: 人工智能, 神经网络, Swin Transformer, 海冰分类, 多年冰, 北极航道, 合同孔径雷达

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)

中图分类号:

P731.15
P714

邵志远, 赵杰臣, 解龙翔, 牟芳如, 肖静, 刘敏君, 陈雪婧. 基于深度学习和Sentinel-1卫星影像的北极海冰分类精度和影响因素[J]. 海洋学研究, 2024, 42(3): 119-130.

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.

图/表 10

参考文献 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

基于深度学习和Sentinel-1卫星影像的北极海冰分类精度和影响因素

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

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参考文献 37

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Metrics

区域	深度学习模型	准确率/%			整体准确率/%	Kappa系数/%
区域	深度学习模型	海水	一年冰	多年冰	整体准确率/%	Kappa系数/%
	ResNet	99.50	100.00	93.75	97.75	96.63
1号	Vision Transformer	98.63	100.00	99.63	98.12	97.19
	Swin Transformer	97.50	100.00	100.00	99.17	98.75
	ResNet	100.00	100.00	94.38	98.12	97.19
2号	Vision Transformer	96.88	99.88	100.00	98.92	98.38
	Swin Transformer	99.25	100.00	99.88	99.71	99.63
	ResNet	100.00	100.00	98.50	99.50	99.25
3号	Vision Transformer	95.25	100.00	98.88	98.42	97.63
	Swin Transformer	99.25	100.00	100.00	99.75	99.63
	ResNet	99.88	99.88	97.88	99.17	98.81
9号	Vision Transformer	98.88	100.00	100.00	99.62	99.44
	Swin Transformer	99.88	99.88	99.25	99.67	99.50
	ResNet	100.00	100.00	97.50	99.62	99.44
10号	Vision Transformer	97.50	100.00	100.00	99.17	98.75
	Swin Transformer	99.50	100.00	100.00	99.75	99.63
	ResNet	100.00	100.00	99.38	99.79	99.69
11号	Vision Transformer	99.25	100.00	100.00	99.83	99.75
	Swin Transformer	100.00	100.00	100.00	100.00	100.00
	ResNet	99.50	100.00	98.00	99.17	98.75
16号	Vision Transformer	95.13	100.00	100.00	98.37	97.56
	Swin Transformer	100.00	99.88	100.00	99.96	99.94
	ResNet	100.00	100.00	93.88	97.96	96.94
18号	Vision Transformer	97.13	100.00	99.88	99.00	98.50
	Swin Transformer	98.63	100.00	99.63	99.42	99.13

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