干旱区生态环境知识资源中心(Arid): Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring

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DOI	10.1016/j.rsase.2021.100643
	Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring
	Shebl, Ali; Csamer, Arpad
通讯作者	Shebl, A (corresponding author), Univ Debrecen, Dept Mineral & Geol, Debrecen, Hungary.
来源期刊	REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT
ISSN	2352-9385
出版年	2021
卷号	24
英文摘要	Machine Learning Algorithms (MLAs) have recently introduced considerable lithologic mapping. Thus, this study scrutinizes the efficacy of Artificial Neural Network (ANN), Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) over hybrid datasets including optical (Sentinel 2, ASTER, Landsat OLI and Earth-observing 1 Advanced Land Imager (ALI)), radar (Sentinel 1 and ALOS PALSAR), DEMs and their derivatives (Slope, and Aspect). The study aims to (1) monitor the effect of data dimensionality in enhancing categorization accuracy. (2) disclose the most efficient MLA and most powerful dataset in labeling rock units accurately. (3) highlight the impact of embedding topographical and radar data in lithologic classification. (4) outline the best relation between the number of training pixels and number of utilized bands, in delivering reliable allocation. To achieve these aims, we selected training and testing pixels meticulously, in concordance with a recently published geological map of the study area. We adopted a stacked vector approach for handling the implemented multi-sensor data. Results show that diversifying information sources raised the classification accuracy by approximately 10% for each classifier. SVM and MLC are much better than ANN. Slope is better than aspect and both are less qualified when compared to DEM. Sentinel 1 (C-band) and ALOS PALSAR (L-band) effects are not so different whatever the implemented polarization. Landsat OLI is less qualified in lithologic classification when compared to Sentinel 2, ASTER and ALI. The utilized training pixels should be at least 30N for (N) channels submitted to the classifiers.
英文关键词	Lithologic classification Support vector machine Artificial neural network Maximum likelihood classifier Sentinel 2 Sentinel 1 ASTER
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	ESCI
WOS记录号	WOS:000719811400005
WOS关键词	EASTERN DESERT ; GOLD MINERALIZATION ; ASTER ; ALI ; HYPERION ; IDENTIFICATION ; KURDISTAN ; IMAGERY ; AREA
WOS类目	Environmental Sciences ; Remote Sensing
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374339
作者单位	[Shebl, Ali; Csamer, Arpad] Univ Debrecen, Dept Mineral & Geol, Debrecen, Hungary; [Shebl, Ali] Tanta Univ, Dept Geol, Tanta, Egypt
推荐引用方式 GB/T 7714	Shebl, Ali,Csamer, Arpad. Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring[J],2021,24.
APA	Shebl, Ali,&Csamer, Arpad.(2021).Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring.REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT,24.
MLA	Shebl, Ali,et al."Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring".REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 24(2021).