人民长江2024，Vol.55Issue(4)：133-141,9.DOI:10.16232/j.cnki.1001-4179.2024.04.018
基于CNN-OBIA的黄河源区水体提取及时空变化
Water extraction of source regions of Yellow River and its spatiotemporal variation based on CNN-OBIA
摘要
Abstract
Accurately identifying water features is a crucial technical means for analyzing the spatiotemporal changes of surface water.In response to the problem of low accuracy in various long-term water extraction methods,we utilized Landsat remote sens-ing imagery to select 5484 scenes of usable imagery from the Yellow River source regions spanning from 1986 to 2022.Two meth-ods,Convolutional Neural Networks(CNN)combined with Object-based Image Analysis(OBIA)and Multi-Index Water Detec-tion Rules(MIWDR),were utilized to extract surface water features in the Yellow River source area.The accuracy of the two methods was compared and analyzed.Subsequently,the spatiotemporal characteristics of water features in the Yellow River source area from 1986 to 2022 were explored,and correlation analysis was conducted to investigate the main climatic factors.The results revealed that:①CNN-OBIA achieved an overall accuracy of 96.78%and a Kappa coefficient of 0.93,while MIWDR achieved an overall accuracy of 94.28%and a Kappa coefficient of 0.88.Overall,CNN-OBIA exhibited higher extraction accuracy than the MIWDR method.CNN-OBIA results better preserved the integrity of water boundaries,effectively removed mountain shad-ows,and improved the accuracy of extracting smaller rivers.② Total water area of the study area showed a decreasing trend in 1986～2001,followed by an increasing trend in 2001～2022.③ Correlation analysis indicated a significant positive correlation between precipitation,temperature,and changes in water area.关键词
水体面积提取/卷积神经网络/面向对象/驱动力分析/黄河源区Key words
water area extraction/convolutional neural networks/object-based image analysis/driving force analysis/source regions of the Yellow River分类
天文与地球科学引用本文复制引用
陈伟,张秀霞,党星海,樊新成,李旺平,徐俊伟..基于CNN-OBIA的黄河源区水体提取及时空变化[J].人民长江,2024,55(4):133-141,9.基金项目
甘肃省教育厅青年博士基金项目(2022QB-052) （2022QB-052）
甘肃省自然科学基金项目(22JR5RA247) （22JR5RA247）
