A fine classification method for sea ice based on random forest combining texture feature and NDVI

Wang Zhiyong; Zhang Mengyue; Yu Yaran; Ni Ping

doi:10.12284/hyxb2021167

Volume 43 Issue 10

Oct. 2021

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Wang Zhiyong，Zhang Mengyue，Yu Yaran, et al. 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

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A fine classification method for sea ice based on random forest combining texture feature and NDVI

doi: 10.12284/hyxb2021167

College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

Received Date: 2020-09-01
Rev Recd Date: 2021-04-15

Available Online: 2021-08-27

Publish Date: 2021-10-30

Abstract

Abstract

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

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References(22)

References

[1]	Su Hua, Wang Yunpeng, Xiao Jie, et al. Improving MODIS sea ice detectability using gray level co-occurrence matrix texture analysis method: A case study in the Bohai Sea[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 85: 13−20. doi: 10.1016/j.isprsjprs.2013.07.010
[2]	Han Yanling, Wei Cong, Zhou Ruyan, et al. Combining 3D-CNN and squeeze-and-excitation networks for remote sensing sea ice image classification[J]. Mathematical Problems in Engineering, 2020, 2020: 8065396.
[3]	Zhang Lu, Liu Huiying, Gu Xinwei, et al. Sea ice classification using TerraSAR-X ScanSAR data with removal of scalloping and interscan banding[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(2): 589−598. doi: 10.1109/JSTARS.2018.2889798
[4]	Lohse J, Doulgeris A P, Dierking W. An optimal decision-tree design strategy and its application to sea ice classification from SAR imagery[J]. Remote Sensing, 2019, 11(13): 1574. doi: 10.3390/rs11131574
[5]	Liu Huiying, Guo Huadong, Zhang Lu. SVM-Based sea ice classification using textural features and concentration from RADARSAT-2 dual-pol ScanSAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(4): 1601−1613. doi: 10.1109/JSTARS.2014.2365215
[6]	Han Yanling, Gao Yi, Zhang Yun, et al. Hyperspectral sea ice image classification based on the spectral-spatial-joint feature with deep learning[J]. Remote Sensing, 2019, 11(18): 2170. doi: 10.3390/rs11182170
[7]	李宝辉, 侯一筠, 孙从容, 等. “北京一号”小卫星图像在渤海海冰监测中的应用[J]. 海洋学报, 2013, 35(4): 201−207. Li Baohui, Hou Yijun, Sun Congrong, et al. The application of “Beijing No. 1” satellite monitoring sea ice images in the Bohai Sea[J]. Haiyang Xuebao, 2013, 35(4): 201−207.
[8]	王姝力, 王志勇, 王磊. 基于Landsat-8和Sentinel-1A辽东湾海冰分类研究[J]. 北京测绘, 2019, 33(12): 1486−1492. Wang Shuli, Wang Zhiyong, Wang Lei. Study of sea ice classification of Landsat-8 and Sentinel-1A in Liaodong Bay[J]. Beijing Surveying and Mapping, 2019, 33(12): 1486−1492.
[9]	韩彦岭, 李鹏, 张云, 等. 主动学习与半监督技术相结合的海冰图像分类[J]. 遥感信息, 2019, 34(2): 15−22. doi: 10.3969/j.issn.1000-3177.2019.02.003 Han Yanling, Li Peng, Zhang Yun, et al. Combining active learning with semi-supervised learning for sea ice image classification[J]. Remote Sensing Information, 2019, 34(2): 15−22. doi: 10.3969/j.issn.1000-3177.2019.02.003
[10]	张明, 吕晓琪, 张晓峰, 等. 结合纹理特征的SVM海冰分类方法研究[J]. 海洋学报, 2018, 40(11): 149−156. Zhang Ming, Lü Xiaoqi, Zhang Xiaofeng, et al. Research on SVM sea ice classification based on texture features[J]. Haiyang Xuebao, 2018, 40(11): 149−156.
[11]	屈猛, 庞小平, 赵羲, 等. 利用多源遥感数据识别波弗特海冰间水道[J]. 武汉大学学报(信息科学版), 2019, 44(6): 917−924. Qu Meng, Pang Xiaoping, Zhao Xi, et al. Detection of sea ice lead in Beaufort Sea based on multisensory remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2019, 44(6): 917−924.
[12]	Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610−621. doi: 10.1109/TSMC.1973.4309314
[13]	Hill M J. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect[J]. Remote Sensing of Environment, 2013, 137: 94−111. doi: 10.1016/j.rse.2013.06.004
[14]	Lei Ruibo, Zhang Zhanhai, Matero I, et al. Reflection and transmission of irradiance by snow and sea ice in the central Arctic Ocean in summer 2010[J]. Polar Research, 2012, 31(1): 17325. doi: 10.3402/polar.v31i0.17325
[15]	Gerland S, Winther J G, Børre Ørbæk J, et al. Physical properties, spectral reflectance and thickness development of first year fast ice in Kongsfjorden, Svalbard[J]. Polar Research, 1999, 18(2): 275−282. doi: 10.1111/j.1751-8369.1999.tb00304.x
[16]	陈前, 郑利娟, 李小娟, 等. 基于深度学习的高分遥感影像水体提取模型研究[J]. 地理与地理信息科学, 2019, 35(4): 43−49. doi: 10.3969/j.issn.1672-0504.2019.04.007 Chen Qian, Zheng Lijuan, Li Xiaojuan, et al. Water body extraction from high-resolution satellite remote sensing images based on deep learning[J]. Geography and Geo-Information Science, 2019, 35(4): 43−49. doi: 10.3969/j.issn.1672-0504.2019.04.007
[17]	Koutsias N, Mallinis G, Karteris M. A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(1): 37−46. doi: 10.1016/j.isprsjprs.2008.06.004
[18]	Rodriguez-Galiano V F, Ghimire B, Rogan J, et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93−104. doi: 10.1016/j.isprsjprs.2011.11.002
[19]	李鹏, 普思寻, 李振洪, 等. 2000年以来胶州湾海岸线光学与SAR多源遥感变化监测研究[J]. 武汉大学学报(信息科学版), 2020, 45(9): 1485−1492. Li Peng, Pu Sixun, Li Zhenhong, et al. Coastline change monitoring of Jiaozhou Bay from multi-source SAR and optical remote sensing images since 2000[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1485−1492.
[20]	Huang Wei, Sun Shunrong, Jiang Haibin, et al. GF-2 satellite 1m/4 m camera design and In-Orbit commissioning[J]. Chinese Journal of Electronics, 2018, 27(6): 1316−1321. doi: 10.1049/cje.2018.09.018
[21]	Lohse J, Doulgeris A P, Dierking W. Incident angle dependence of Sentinel-1 texture features for sea ice classification[J]. Remote Sensing, 2021, 13(4): 552. doi: 10.3390/rs13040552
[22]	罗丽程. 基于Sentinel-1数据的北极海冰提取及分类研究[D]. 上海: 中国科学院大学(中国科学院上海技术物理研究所), 2018. Luo Licheng. Research on the extraction and classification of Arctic sea ice based on Sentinel-1 data[D]. Shanghai: University of Chinese Academy of Sciences (Shanghai Institute of Technical Physics of the Chinese Academy of Sciences), 2018.