干旱区生态环境知识资源中心(Arid): Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms

Arid

DOI	10.3390/app14125064
	Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
	Ali, Sajid; Li, Huan; Ali, Asghar; Hassan, Jubril Izge
通讯作者	Li, H
来源期刊	APPLIED SCIENCES-BASEL
EISSN	2076-3417
出版年	2024
卷号	14 期号:12
英文摘要	In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.
英文关键词	remote sensing machine learning SVM MLC lithology ASTER OLI Khyber
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:001254436200001
WOS关键词	SPACEBORNE THERMAL EMISSION ; CENTRAL ANTI-ATLAS ; OPHIOLITE COMPLEX ; HYDROTHERMAL ALTERATION ; RANDOM FORESTS ; ASTER DATA ; CLASSIFICATION ; AREA ; MINERALS ; DEPOSITS
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402877
推荐引用方式 GB/T 7714	Ali, Sajid,Li, Huan,Ali, Asghar,et al. Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms[J],2024,14(12).
APA	Ali, Sajid,Li, Huan,Ali, Asghar,&Hassan, Jubril Izge.(2024).Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms.APPLIED SCIENCES-BASEL,14(12).
MLA	Ali, Sajid,et al."Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms".APPLIED SCIENCES-BASEL 14.12(2024).