一种融合纹理特征与NDVI的随机森林海冰精细分类方法

王志勇; 张梦悦; 于亚冉; 泥萍

doi:10.12284/hyxb2021167

一种融合纹理特征与NDVI的随机森林海冰精细分类方法

doi: 10.12284/hyxb2021167

山东科技大学测绘与空间信息学院，山东青岛 266590

基金项目: 国家自然科学基金（41876202）；山东省自然科学基金（ZR2017MD020）

详细信息

作者简介:
王志勇（1978-），男，山东省青岛市人，博士，副教授，主要从事雷达干涉测量、海洋遥感等方面的研究。E-mail：wzywlp@163.com

通讯作者:
张梦悦（1996-），硕士生，主要从事光学海冰分类方面的研究。E-mail：zhang19961028@126.com

中图分类号: P731.15; P715.7
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- 被引次数: 0
出版历程
- 收稿日期: 2020-09-01
- 修回日期: 2021-04-15
- 网络出版日期: 2021-08-27
- 刊出日期: 2021-10-30

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

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

摘要

摘要: 海冰的精准分类对于掌握海冰生长发育状况，保障航海安全等具有重要意义。由于受数据源和分类方法等影响，使得海冰分类精度提高受限。本文面向高空间分辨率的光学遥感影像，提出了一种融合纹理特征和归一化差分植被指数（NDVI）的海冰精准分类方法，运用随机森林分类器构建海冰分类方法。以青岛胶州湾为实验区，高分二号（GF-2）为实验数据，进行了海冰类型提取，并与其他分类方法进行对比。结果显示：针对GF-2高分辨率光学遥感数据，融合纹理特征和NDVI的随机森林方法，相比于传统的随机森林、支持向量机、自动决策树和融合纹理特征的最大似然分类方法，总体分类精度分别提高13.70%、11.60%、19.22%、29.37%。Kappa系数分别提高0.16、0.13、0.22、0.44。相比于融合纹理特征和归一化水指数（NDWI）的随机森林方法，总体分类精度提高了9.67%，Kappa系数提高了0.09。这表明本文构建的海冰分类方法可有效提高海冰分类精度，为海冰的精确分类提供了一种有效的技术手段。
- 海冰分类 /
- GF-2影像 /
- 随机森林 /
- 纹理特征 /
- NDVI
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

HTML全文

图 1 流程图

Fig. 1 Flow chart

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图 2 研究区域位置

Fig. 2 Location of study area

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图 3 不同海冰类型的灰度共生矩阵特征折线图

Fig. 3 The gray-level co-occurrence matrix characteristic line chart of different sea ice types

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图 4 6种分类算法结果对比

Fig. 4 Comparison results of six classification algorithms

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图 5 海冰分类局部放大图

Fig. 5 Sea ice classification and partial enlargement

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表 1 主成分分析结果

Tab. 1 Results of principal component analysis

PC	特征值	累计特征值百分比/%	特征值百分比/%
1	3.848 8	96.22	96.22
2	0.146 7	99.89	3.67
3	0.004 2	99.99	0.10
4	0.000 4	100.00	0.01

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表 2 不同分类算法精度

Tab. 2 Accuracy of different classification algorithms

方法	海冰类别	制图精度/%	用户精度/%	总体精度/%	Kappa 系数
融合纹理特征和 NDVI的RF	冰皮	26.50	10.36	84.68	0.73
	灰冰	94.83	44.84
	白冰	92.34	99.88
	海水	84.76	94.08
融合纹理特征和 NDWI的RF	冰皮	89.33	20.98	75.01	0.64
	灰冰	86.96	42.21
	白冰	92.62	90.45
	海水	62.45	98.24
传统RF	冰皮	97.20	22.66	70.98	0.57
	灰冰	96.15	9.78
	白冰	98.76	100.00
	海水	58.56	99.78
SVM	冰皮	92.91	15.20	73.08	0.60
	灰冰	88.35	28.57
	白冰	98.54	100.00
	海水	60.88	99.17
CART	冰皮	99.23	14.85	65.46	0.51
	灰冰	99.58	14.55
	白冰	98.70	100.00
	海水	57.58	99.92
融合纹理特征的ML	冰皮	90.17	13.18	55.31	0.29
	灰冰	67.95	59.58
	白冰	100.00	54.24
	海水	55.24	67.33

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