基于无人机影像的城市植被精细分类

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Abstract The fine classification and information extraction of urban public vegetation based on remote sensing methods hasgreat significance in urban land use change, urban ecological environment monitoring and urban planning. Aiming toreducethe heavy workload, high cost and enhancetheefficiency of traditional vegetation resource survey methods, a multi-dimensional feature space including spectral, spatial and texture features for describing different types of urban vegetation was proposed in this study. Then a comparison experiment of three typical classification algorithms (pixel-based, object-oriented support vector machine and deep learning semantic segmentation model-Mobile-Unet) was accomplished. The experimental results indicate that:The proposed spectrum-texture-spatial feature extraction method can describe different types of urban vegetation effectively, and improve the accuracy of image segmentation and vegetation classification significantly. In terms of classification accuracy, the overall accuracy of pixel-based and object-oriented support vector machine classification exceed 90%, while the overall accuracy of deep learning method is only 84%. In terms of efficiency, traditional machine learning methods are also superior to deep learning method. Therefore, for the fine classification of different vegetation in small urban areas with small samples, traditional machine learning classification methods are more effective than deep learning method.

Key words： unmanned aerial vehicle image urban vegetation fine classification multi-dimensional feature space machine learning deep learning

Received: 17 November 2021

PACS:	TP751.1
	X87

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	Articles by authors
	LIN Yi
	ZHANG Wen-hao
	YU Jie
	ZHANG Han-chao

Cite this article:

LIN Yi,ZHANG Wen-hao,YU Jie等. Fine classification of urban vegetation based on UAV images[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(6): 2852-2861.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I6/2852

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