干旱区生态环境知识资源中心(Arid): Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers

Arid

DOI	10.3390/rs14215498
	Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers
	Serbouti, Imane; Raji, Mohammed; Hakdaoui, Mustapha; El Kamel, Fouad; Pradhan, Biswajeet; Gite, Shilpa; Alamri, Abdullah; Maulud, Khairul Nizam Abdul; Dikshit, Abhirup
通讯作者	Pradhan, B
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:21
英文摘要	In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km(2) area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), naive Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster-Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.
英文关键词	machine learning algorithms google earth engine dempster-shafer theory lithological mapping Sentinel 2A Moroccan Meseta
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000884194200001
WOS关键词	REMOTE-SENSING DATA ; SUPPORT VECTOR MACHINES ; SPATIAL-RESOLUTION ; MULTISPECTRAL DATA ; ASTER DATA ; CLASSIFICATION ; FEATURES ; AREA ; OLI ; CARTOGRAPHY
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394225
推荐引用方式 GB/T 7714	Serbouti, Imane,Raji, Mohammed,Hakdaoui, Mustapha,et al. Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers[J],2022,14(21).
APA	Serbouti, Imane.,Raji, Mohammed.,Hakdaoui, Mustapha.,El Kamel, Fouad.,Pradhan, Biswajeet.,...&Dikshit, Abhirup.(2022).Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers.REMOTE SENSING,14(21).
MLA	Serbouti, Imane,et al."Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers".REMOTE SENSING 14.21(2022).