干旱区生态环境知识资源中心(Arid): Land use/land cover recognition in arid zone using A multi-dimensional multi-grained residual Forest

Arid

DOI	10.1016/j.cageo.2020.104557
	Land use/land cover recognition in arid zone using A multi-dimensional multi-grained residual Forest
	Weng, Liguo; Qian, Ming; Xia, Min; Xu, Yiqing; Li, Chunzheng
通讯作者	Xia, M
来源期刊	COMPUTERS & GEOSCIENCES
ISSN	0098-3004
EISSN	1873-7803
出版年	2020
卷号	144
英文摘要	Monitoring arid areas could effectively improve economic, ecological and humanity benefits. It is an effective monitoring approach to recognize the land cover or land use of arid areas through machine learning methods using satellite images. However, there is no public classified dataset for arid areas currently, and hence remote sensing image monitoring in desert areas is restricted. Existing classification methods are not able to fully utilize effective features of satellite images and multi-spectral optical parameters. In this paper, our contributions are as follows: Firstly, we presented a new satellite dataset named the ARID-5 for arid area land cover/land use (LULC) classification, the LULC in arid areas included desert, oasis, Gobi, and water system. Second, we proposed a machine learning algorithm named the multi-dimensional multi-grained residual forest algorithm for LULC recognition on arid areas. In this algorithm, the multi-dimensional multi-grained structure was able to effectively extract image features and spectral information. The residual forest structure mapped probability feature vectors to higher levels for prediction, which effectively improved the reflection of the forest structure on the sample. At the same time, the base estimator was transmitted in cascade layers, and thus the diversity and the accuracy were improved. Experimental results proved that the multi-dimensional multi-grained residual forest showed good classification abilities. Last, we also tested our algorithm on SAT-4 and SAT-6 datasets, which proved the generalization performance of our algorithm.
英文关键词	Arid area Satellite image Multi-dimensional multi-grained residual forest Land use/land cover recognition Machine learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000580574900003
WOS关键词	CLASSIFICATION ; DESERT ; SEGMENTATION ; LANDSCAPE ; XINJIANG ; DATASET ; REGION
WOS类目	Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS研究方向	Computer Science ; Geology
来源机构	南京信息工程大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326982
作者单位	[Weng, Liguo; Qian, Ming; Xia, Min; Li, Chunzheng] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China; [Xu, Yiqing] Nanjing Forestry Univ, Nanjing 210095, Peoples R China
推荐引用方式 GB/T 7714	Weng, Liguo,Qian, Ming,Xia, Min,et al. Land use/land cover recognition in arid zone using A multi-dimensional multi-grained residual Forest[J]. 南京信息工程大学,2020,144.
APA	Weng, Liguo,Qian, Ming,Xia, Min,Xu, Yiqing,&Li, Chunzheng.(2020).Land use/land cover recognition in arid zone using A multi-dimensional multi-grained residual Forest.COMPUTERS & GEOSCIENCES,144.
MLA	Weng, Liguo,et al."Land use/land cover recognition in arid zone using A multi-dimensional multi-grained residual Forest".COMPUTERS & GEOSCIENCES 144(2020).