干旱区生态环境知识资源中心(Arid): Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries

Arid

DOI	10.3390/s22186844
	Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries
	Liu, Ximing; Samat, Alim; Li, Erzhu; Wang, Wei; Abuduwaili, Jilili
通讯作者	Samat, A
来源期刊	SENSORS
EISSN	1424-8220
出版年	2022
卷号	22 期号:18
英文摘要	Impervious surface area (ISA) has been recognized as a significant indicator for evaluating levels of urbanization and the quality of urban ecological environments. ISA extraction methods based on supervised classification usually rely on a large number of manually labeled samples, the production of which is a time-consuming and labor-intensive task. Furthermore, in arid areas, man-made objects are easily confused with bare land due to similar spectral responses. To tackle these issues, a self-trained deep-forest (STDF)-based ISA extraction method is proposed which exploits the complementary information contained in multispectral and polarimetric synthetic aperture radar (PolSAR) images using limited numbers of samples. In detail, this method consists of three major steps. First, multi-features, including spectral, spatial and polarimetric features, are extracted from Sentinel-2 multispectral and Chinese GaoFen-3 (GF-3) PolSAR images; secondly, a deep forest (DF) model is trained in a self-training manner using a limited number of samples for ISA extraction; finally, ISAs (in this case, in three major cities located in Central Asia) are extracted and comparatively evaluated. The experimental results from the study areas of Bishkek, Tashkent and Nursultan demonstrate the effectiveness of the proposed method, with an overall accuracy (OA) above 95% and a Kappa coefficient above 0.90.
英文关键词	impervious surface area self-training deep forest Sentinel-2 GaoFen-3 PolSAR
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000857093700001
WOS关键词	SPECTRAL MIXTURE ANALYSIS ; COMPOSITION INDEX ; NIGHTTIME LIGHT ; SYNERGISTIC USE ; SAR ; CLASSIFICATION ; DYNAMICS ; INTEGRATION ; CHINA ; MODEL
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394484
推荐引用方式 GB/T 7714	Liu, Ximing,Samat, Alim,Li, Erzhu,et al. Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries[J],2022,22(18).
APA	Liu, Ximing,Samat, Alim,Li, Erzhu,Wang, Wei,&Abuduwaili, Jilili.(2022).Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries.SENSORS,22(18).
MLA	Liu, Ximing,et al."Self-Trained Deep Forest with Limited Samples for Urban Impervious Surface Area Extraction in Arid Area Using Multispectral and PolSAR Imageries".SENSORS 22.18(2022).