Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning

doi:10.15244/pjoes/160190

	Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning
	Ma, Hong 1; Zhao, Wenju1 ; Li, Fenhua 2; Yan, Honghua 2; Liu, Yuhang 1
	2023
发表期刊	POLISH JOURNAL OF ENVIRONMENTAL STUDIES
ISSN	1230-1485
卷号	32 期号:3 页码:2231-2242
摘要	In this paper, based on the Landsat multispectral remote sensing images of 1999, 2008 and 2019 in the oasis area of the Taolai River Basin, a remote sensing image classification method based on ENVI deep learning was constructed to extract and identify the cover information of oasis area on the basis of establishing classification system, interpretation flags and sample data sets, and compared with the classification methods based on backpropagation neural network (BPNN), support vector machine regression (SVM) and random forest (RF). The results show that the overall accuracy of the classification method based on ENVI deep learning is 97.34 %, and the Kappa coefficient is 0.96; Under the same number of samples, compared with the classification method based on BPNN, SVM and RF, the classification method based on ENVI deep learning constructed in this study improves the overall accuracy by 6.80%, 2.04% and 3.03%, and the Kappa coefficient increases by 0.12, 0.07 and 0.09, respectively, and the classification method is the best for extracting surface cover information fin oasis area. This study can provide technical support for rapid and accurate extraction and identification of ground cover information.
关键词	remote sensing image classification method Kappa coefficient deep learning Oasis area
DOI	10.15244/pjoes/160190
收录类别	SCIE
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:000972727900022
出版者	HARD
来源库	WOS
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/162113
专题	能源与动力工程学院
作者单位	1.Lanzhou Univ Technol, Coll Energy & Power Engn, Lanzhou 730050, Peoples R China; 2.Taolai River Basin Water Resources Utilizat Ctr, Gansu Prov Dept Water Resources, Jiuquan 735000, Peoples R China
第一作者单位	能源与动力工程学院
第一作者的第一单位	能源与动力工程学院
推荐引用方式 GB/T 7714	Ma, Hong,Zhao, Wenju,Li, Fenhua,et al. Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning[J]. POLISH JOURNAL OF ENVIRONMENTAL STUDIES,2023,32(3):2231-2242.
APA	Ma, Hong,Zhao, Wenju,Li, Fenhua,Yan, Honghua,&Liu, Yuhang.(2023).Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning.POLISH JOURNAL OF ENVIRONMENTAL STUDIES,32(3),2231-2242.
MLA	Ma, Hong,et al."Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning".POLISH JOURNAL OF ENVIRONMENTAL STUDIES 32.3(2023):2231-2242.