干旱区生态环境知识资源中心(Arid): Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm

Arid

DOI	10.1016/j.jag.2023.103318
	Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm
	Lu, Jiaxin; Han, Ling; Liu, Lei; Wang, Junfeng; Xia, Zhaode; Jin, Dingjian; Zha, Xinlin
通讯作者	Han, L
来源期刊	INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
ISSN	1569-8432
EISSN	1872-826X
出版年	2023
卷号	119
英文摘要	The development of multi-source remote sensing technologies is helpful for geologists to obtain more compre-hensive and complete lithological maps. In recent years, establishing automatic classification models based on Machine Learning (ML) algorithms has become an important approach to identify various lithologies supported by remote sensing data. Aiming at the specific geological and geographical conditions in a semi-arid area, Duolun County, Inner Mongolia Autonomous Region, China, this paper integrated GaoFen-2 (GF-2), Sentinel-2A, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and GaoFen-3 (GF-3) remote sensing data, and used Support Vector Machine (SVM) classifier on the basis of Particle Swarm Optimization (PSO) to carry out the lithology classification. Firstly, on the basis of removing the interference of vegetation information from the acquired remote sensing data, a 63-dimensional candidate feature sequence was con-structed by extracting spectral, backscattering, polarization and texture features. Secondly, an improved PSO algorithm with the Inertia Factor changing with the S-curve Decreasing function (SDIF-PSO) was proposed, and on this basis, a feature selection and lithology classification algorithm using SVM classifier based on two-layer SDIF-PSO was designed. Finally, the iterative optimization process of multiple optimization algorithms for SVM model parameters and the lithology classification accuracy before and after feature selection were compared. The experimental results showed that the proposed SDIF-PSO algorithm had the best optimization capability, with the highest cross-validation accuracy of 90.90%, which was improved by 3.85% than that of Grid-Search Optimization (GSO) algorithm, and 0.15% than that of the improved PSO algorithm with the Inertia Factor changing with the Linear Decreasing function (LDIF-PSO) and the improved PSO algorithm with the Inertia Factor Decreasing with the Concave function (CDIF-PSO). The dimension of the best feature combination was reduced to 35 through feature selection, and the convergence cross-validation accuracy reaches 92.14%, which was improved by 1.24% than that of all 63-dimensional candidate features in the same optimization process using SDIF-PSO algorithm.
英文关键词	Multi-source remote sensing Particle swarm optimization Feature selection Semi-arid area Lithology classification
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:000989942700001
WOS关键词	VEGETATION ; RADAR
WOS类目	Remote Sensing
WOS研究方向	Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396961
推荐引用方式 GB/T 7714	Lu, Jiaxin,Han, Ling,Liu, Lei,et al. Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm[J],2023,119.
APA	Lu, Jiaxin.,Han, Ling.,Liu, Lei.,Wang, Junfeng.,Xia, Zhaode.,...&Zha, Xinlin.(2023).Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,119.
MLA	Lu, Jiaxin,et al."Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 119(2023).