气候变化领域集成服务门户(CCPortal): ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores

CCPortal

DOI	10.1109/ACCESS.2023.3341156
	ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores
	Deo, Ratneel; Webster, Jody M.; Salles, Tristan; Chandra, Rohitash
发表日期	2024
ISSN	2169-3536
起始页码	12
卷号	12
英文摘要	Coral reefs are among the most biologically diverse and economically valuable ecosystems on Earth, but they are threatened by climate change. Understanding how reefs developed over geological timescales can provide important information about past environmental changes and their impacts on reef systems. Significant effort and capital have been invested in drilling and analyzing reef cores. Recognizing coral and sediment patterns visually from fossil reefs is a laborious task that demands domain expertise. In this paper, we present a machine learning-based framework that utilizes clustering and classification methods to fuse multiple sources of data for the segmentation and annotation of reef cores. The framework produces an annotated image of a reef core with six lithologies identified; massive corals, encrusted corals, coralline algae, microbialite, sand, and silt. We utilize reef cores recovered from Expedition 325 of the International Ocean Discovery Program (IODP) to the Great Barrier Reef. We use reef core image data and physical properties data to segment reef cores. We evaluate the framework using selected clustering and classification models. The results show that Gaussian mixture models can provide accurate segmentation of reef core image data, with a clear visual distinction between two major classes: massive corals and stromatolitic microbialites. Furthermore, we find that the random forest classifier provides the best annotations for the segmented reef core image data with an accuracy of 96%.
英文关键词	Clustering; segmentation; multi-source data; classification; reef core analysis; Gaussian mixture models
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001151612700001
来源期刊	IEEE ACCESS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297351
作者单位	University of Sydney; University of New South Wales Sydney; University of New South Wales Sydney
推荐引用方式 GB/T 7714	Deo, Ratneel,Webster, Jody M.,Salles, Tristan,et al. ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores[J],2024,12.
APA	Deo, Ratneel,Webster, Jody M.,Salles, Tristan,&Chandra, Rohitash.(2024).ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores.IEEE ACCESS,12.
MLA	Deo, Ratneel,et al."ReefCoreSeg: A Clustering-Based Framework for Multi-Source Data Fusion for Segmentation of Reef Drill Cores".IEEE ACCESS 12(2024).