气候变化领域集成服务门户(CCPortal): Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors

CCPortal

DOI	10.1016/j.rse.2020.112209
	Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors
	Cordeiro M.C.R.; Martinez J.-M.; Peña-Luque S.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Continuous monitoring of water surfaces is essential for water resource management. This study presents a nonparametric unsupervised automatic algorithm for the identification of inland water pixels from multispectral satellite data using multidimensional clustering and a high-performance subsampling approach for large scenes. Clustering analysis is a technique that is used to identify similar samples in a multidimensional data space. The spectral information and derived indices were used to characterize each scene pixel individually. A machine learning approach with random subsampling and generalization through a Naïve Bayes classifier was also proposed to make the application of complex algorithms to large scenes feasible. Accuracy was evaluated using an independent dataset that provides water bodies in 15 Sentinel-2 images over France acquired in different seasons and that covers a large range of water bodies and water colour types. The validation dataset covers a water surface of more than 1200 km2 (approximately 12 million pixels) including over 80,000 water bodies outlined using a semiautomatic active learning method, which were manually revised. The classification results were compared to the water pixel classification using three of the major Level 2A processors (MAJA, Sen2Cor and FMask) and two of the most common thresholding techniques: Otsu and Canny-edge. An input mask was used to remove coastal waters, clouds, shadows and snow pixels. Water pixels were identified automatically from the clustering process without the need for ancillary or pretrained data. Combinations using up to three water indices (Modified Normalized Difference Water Index-MNDWI, Normalized Difference Water Index-NDWI and Multiband Water Index-MBWI) and two reflectance bands (B8 and B12) were tested in the algorithm, and the best combination was NDWI-B12. Of all the methods, our method achieved the highest mean kappa score, 0.874, across all tested scenes, with a per-scene kappa ranging from 0.608 to 0.980, and the lowest mean standard deviation of 0.091. Standard Otsu's thresholding had the worst performance due to the lack of a bimodal histogram, and the Canny-edge variation achieved an overall kappa of 0.718 when used with the MNDWI. For water masks provided by generic processors, FMask outperformed MAJA and Sen2Cor and obtained an overall kappa of 0.764. In-depth analysis shows a quick drop in performance for all of the methods in identifying water bodies with a surface area below 0.5 ha, but the proposed approach outperformed the second best method by 34% in this size class. © 2020 Elsevier Inc.
英文关键词	Machine learning; naïve bayes classifier; Sentinel-2; Unsupervised clustering; Water detection; Water mask
语种	英语
scopus关键词	Clustering algorithms; Large dataset; Machine learning; Pixels; Surface waters; Turing machines; Water management; Machine learning approaches; Mean standard deviation; Multidimensional clustering; Multidimensional hierarchical clustering; Multispectral satellite data; Normalized difference water index; Thresholding techniques; Waterresource management; Hierarchical clustering; algorithm; data set; detection method; machine learning; satellite data; Sentinel; surface area; water resource; France
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/179009
作者单位	Agência Nacional de Águas (ANA), Setor Policial Sul, Área 5, Quadra 3, Brasília (DF), 70610-200, Brazil; Géosciences Environnement Toulouse (GET), Unité Mixte de Recherche 5563, IRD/CNRS/Université, Toulouse, 31400, France; Centre National d'Etudes Spatiales (CNES), Toulouse, 31401, France
推荐引用方式 GB/T 7714	Cordeiro M.C.R.,Martinez J.-M.,Peña-Luque S.. Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors[J],2021,253.
APA	Cordeiro M.C.R.,Martinez J.-M.,&Peña-Luque S..(2021).Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors.Remote Sensing of Environment,253.
MLA	Cordeiro M.C.R.,et al."Automatic water detection from multidimensional hierarchical clustering for Sentinel-2 images and a comparison with Level 2A processors".Remote Sensing of Environment 253(2021).