多源时序遥感影像与GEE支持下的潮沟提取方法

doi:10.13474/j.cnki.11-2246.2025.1008

摘要/Abstract

摘要： 本文针对江苏中部海岸带潮沟提取问题,使用了一种基于Sentinel-1和Sentinel-2A高空间分辨率多光谱数据的多源时序遥感影像,结合Google Earth Engine(GEE)平台的半自动提取方法。该方法融合了光谱、指数、纹理、极化等特征,采用随机森林监督分类算法进行了潮沟提取。通过时间序列分析增强多光谱影像信息,计算多种植被指数和水体指数,提取灰度共生矩阵纹理特征,并协同多种特征进行分类分析。研究结果表明,该协同分类方法的潮沟提取总体精度达0.95,优于单一数据源方法,提供较为准确的潮沟边界信息。本文可以为河口潮沟区域的生态保护和可持续管理提供技术支持。

关键词: 潮沟提取, 遥感影像, Sentinel卫星, 随机森林, 协同分类

Abstract: This study focuses on tidal creek extraction in the central Jiangsu coastal zone using a semi-automatic method based on multi-source time-series remote sensing imagery, including Sentinel-1 and Sentinel-2A data, within the Google Earth Engine (GEE)platform.The method integrates spectral, index, texture, and polarization features, employing a Random Forest supervised classification algorithm for extraction.Time-series analysis enhances multispectral data, and various vegetation and water indices, along with texture features, are calculated for classification.The results show an overall accuracy of 0.95, outperforming single-data-source methods and providing accurate tidal creek boundary information.This approach offers technical support for ecological conservation and sustainable management in estuarine tidal creek regions.

Key words: tidal channel extraction, remote sensing imagery, Sentinel satellites, random forest, collaborative classification

中图分类号:

P237

童皓东, 周钰炜, 沈永明. 多源时序遥感影像与GEE支持下的潮沟提取方法[J]. 测绘通报, 2025, 0(10): 43-49.

TONG Haodong, ZHOU Yuwei, SHEN Yongming. A method for tidal creek extraction using multi-source time-series remote sensing and Google Earth Engine[J]. Bulletin of Surveying and Mapping, 2025, 0(10): 43-49.

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