气候变化领域集成服务门户(CCPortal): River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method

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DOI	10.1016/j.rse.2021.112297
	River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method
	Jin S.; Liu Y.; Fagherazzi S.; Mi H.; Qiao G.; Xu W.; Sun C.; Liu Y.; Zhao B.; Fichot C.G.
发表日期	2021
ISSN	00344257
卷号	255
英文摘要	River networks are important water carriers that provide a multitude of ecosystem services, including freshwater for agriculture, drinking water for cities, and recreational activities. Accurate mapping of river networks from remote-sensing images is important for the study of these systems. Unfortunately, the delineation of river networks is challenging due to the meandering nature of river channels, the often complex and variable features of the surrounding landscape, and the spatial heterogeneity of the river networks. Here, we present an adaptive, multi-scale region growth method (AMRGM) to delineate river networks from sentinel-2A/B MSI images. The method can handle variable river heterogeneous surroundings, multiple spatial scales, and variable curvatures of the river channels. The method includes four steps: (1) a water index (NDWI) is used to provide initial detection of river water pixels in the image; (2) a bias-corrected fuzzy C-means (BCFCM) method alleviates the effects of the variable surrounding landscape; (3) a scale-enhancement algorithm based on the hessian matrix makes full use of scale and direction information to enhance river morphology characteristics (multiple dimensions and variable curvatures), and (4) a regional growth criterion facilitates handling of various river dimensions. Fast-growing and fine-screening strategies are also included in the AMRGM. The method is applied to eight river networks to evaluate its accuracy and reliability with various river morphologies and hydrological conditions. The AMRGM is more widely applicable than four commonly used river-detection methods (i.e., K-means method, maximum likelihood method, iterative self-organizing data analysis technique algorithm, and support vector machine) and outperform these methods when detecting multi-scale river branches. The mean overall accuracy (OA) and kappa coefficients (KC) of the AMRGM exceed 97% and 0.92 across the eight river networks. The most accurate river extractions are obtained for large rivers such as the Amazon River, Mackenzie River, and Ganges River Delta, which have more discernable scale and direction characteristics. Relatively high omission and commission errors are observed in river networks with complex and heterogeneous zonations, such as the river Welland, UK, and the Zagya Zangbo River in the Tibet plateau. The complex geomorphic features of the river Welland reduce OA and KC to 93.8% and 0.86, respectively © 2021 Elsevier Inc.
英文关键词	Adaptive multi-scale region growth method (AMRGM); Background homogenization; Fast-growing; Fine-screening; Hessian matrix; Sentinel-2A/B MSI
语种	英语
scopus关键词	Agricultural robots; Binary alloys; Complex networks; Ecosystems; Image enhancement; Iterative methods; K-means clustering; Matrix algebra; Maximum likelihood estimation; Potable water; Remote sensing; Support vector machines; Uranium alloys; Direction characteristic; Enhancement algorithms; Hydrological condition; Iterative self-organizing data analysis techniques; Maximum likelihood methods; Omission and commission errors; Recreational activities; Spatial heterogeneity; Rivers; accuracy assessment; complexity; drinking water; ecosystem service; fuzzy mathematics; heterogeneity; river channel; river system; satellite data; satellite imagery; Sentinel; Amazon River; Canada; Ganges Delta; Mackenzie River [Northwest Territories]; Northwest Territories
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178961
作者单位	School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu Province 210046, China; Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resources of China, Nanjing University, Nanjing, 210023, China; Department of Earth and Environment, Boston University, Boston, MA 02215, United States; College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China; Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, Zhejiang Province 315211, China
推荐引用方式 GB/T 7714	Jin S.,Liu Y.,Fagherazzi S.,et al. River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method[J],2021,255.
APA	Jin S..,Liu Y..,Fagherazzi S..,Mi H..,Qiao G..,...&Fichot C.G..(2021).River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method.Remote Sensing of Environment,255.
MLA	Jin S.,et al."River body extraction from sentinel-2A/B MSI images based on an adaptive multi-scale region growth method".Remote Sensing of Environment 255(2021).