联合时间序列相干性和后向散射系数的黄河三角洲湿地分类

doi:10.13474/j.cnki.11-2246.2023.0040

测绘通报 ›› 2023, Vol. 0 ›› Issue (2): 52-57,71.doi: 10.13474/j.cnki.11-2246.2023.0040

联合时间序列相干性和后向散射系数的黄河三角洲湿地分类

李振今, 王志勇, 叶凯乐, 刘晓彤, 田康

山东科技大学测绘与空间信息学院, 山东青岛 266590

收稿日期:2022-02-14 修回日期:2022-11-10 发布日期:2023-03-01
通讯作者: 王志勇。E-mail:skd994177@sdust.edu.cn
作者简介:李振今(1998-),男,硕士生,主要从事微波遥感。E-mail:zhenjindahaoren@163.com
基金资助:
山东省重大科技创新工程(2019JZZY020103);国家自然科学基金(41876202)

Combined time series coherence and backscattering coefficient for wetland classification in the Yellow River Delta

LI Zhenjin, WANG Zhiyong, YE Kaile, LIU Xiaotong, TIAN Kang

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

Received:2022-02-14 Revised:2022-11-10 Published:2023-03-01

摘要/Abstract

摘要： 针对后向散射系数难以完成高精度湿地分类问题,本文以16景VH极化的Sentinel-1A影像为数据源,构建了一种联合时间序列相干性和后向散射系数的分类方法。通过对长时间序列的后向散射系数和相干性分析,选择互花米草易与其他地物混淆的3个时相(6月27日(R)、11月18日(G)、11月30日(B))的后向散射系数图为合成数据源,引入11月18-30日相干图代替11月30日后向散射系数图。采用SVM和随机森林分类器,探究相干性引入前后黄河三角洲湿地分类精度变化。研究表明,相干性引入后,SVM和随机森林分类结果的总体精度分别提升了3.07%和3.85%,互花米草的分类精度分别提升了9.39%和11.42%。

关键词: 湿地分类, 相干性, 后向散射系数, 黄河三角洲, 互花米草

Abstract: To solve the problem that the backscattering coefficient is difficult to complete the high-precision wetland classification, this paper takes 16 VH Sentinel-1A images as the data source, and constructs a classification method combining time series coherence and backscattering coefficient. By analyzing the long time series backscattering coefficient and coherence, the backscattering coefficients map in three times (June 27 (R), November 18 (G) and November 30 (B)) that Spartina alterniflora is easily confused with other ground objects are selected as the synthetic data sources. Then the coherence map from November 18 to November 30 is introduced to replace the backscattering coefficients map in November 18. SVM and RF classifier are used to explore the accuracy variation before and after introducing coherence in the Yellow River Delta wetlands. The results show that the overall accuracy of classification results by SVM and RF improves by 3.07% and 3.85%, and the accuracy of Spartina alterniflora improves by 9.39% and 11.42%.

Key words: wetland classification, coherence, backscattering coefficient, Yellow River Delta, spartina alterniflora

中图分类号:

P237

李振今, 王志勇, 叶凯乐, 刘晓彤, 田康. 联合时间序列相干性和后向散射系数的黄河三角洲湿地分类[J]. 测绘通报, 2023, 0(2): 52-57,71.

LI Zhenjin, WANG Zhiyong, YE Kaile, LIU Xiaotong, TIAN Kang. Combined time series coherence and backscattering coefficient for wetland classification in the Yellow River Delta[J]. Bulletin of Surveying and Mapping, 2023, 0(2): 52-57,71.

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联合时间序列相干性和后向散射系数的黄河三角洲湿地分类

Combined time series coherence and backscattering coefficient for wetland classification in the Yellow River Delta

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