基于深度学习融合OBIA的黄土高原小流域淤地坝系提取

doi:10.12118/j.issn.1000-6060.2023.057

Abstract

Abstract:

Check dams play an irreplaceable role in preventing and controlling soil erosion in the Loess Plateau of China. Accurately extracting check dam areas and their locations holds immense importance for soil and water conservation studies in the Loess Plateau of China. However, existing image classification methods lack consideration of the topographic features of check dams, leading to many misidentifications. This study proposes a method that combines object-based image analysis (OBIA) and deep learning to precisely identify check dam areas and their locations. First, a multiscale segmentation method based on an object-oriented approach is used to segment the multisource data. Deep learning is applied to identify check dams. The results from these processes were fused using majority voting to enhance the accuracy of check dam area extraction. Second, the river network within the basin is extracted based on digital elevation model data. Finally, the check dam areas and river network are intersected to determine the check dam locations. The results show that the precision and recall rates of this method are 81.97% and 90.94%, respectively, and an F1Score value of 89.70%, which is 21.94% higher than the methods using only OBIA. Additionally, the accuracy and completeness of automatically identified check dam locations are 81.08% and 88.89%, respectively. This method provides important data for the optimal spatial layout of check dams in the Loess Plateau and for the evaluation of soil and water loss.

Key words: silt range extraction, extraction of check dam points, object-bases image analysis (OBIA), U-Net framework, Loess Plateau

QIAN Wei, WANG Chun, DAI Wen, LU Wangda, LI Min, TAO Yu, LI Mengqi. Extraction of check dam system in small watershed of Loess Plateau based on deep learning and OBIA[J].Arid Land Geography, 2023, 46(11): 1803-1812.

Figures/Tables 9

Fig. 1

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Tab. 1

Confusion matrix"

实际类	预测类
实际类	淤地范围	非淤地范围
淤地范围	$T P$	$F N$
非淤地范围	$F P$	$T N$

Tab. 1

Fig. 5

Tab. 2

Fig. 6

Fig. 7

References 26

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提取方法	准确率	精准率	召回率	F1Score
深度学习融合OBIA方法	98.98	81.97	90.94	89.70
OBIA方法	96.67	96.52	52.20	67.76

Extraction of check dam system in small watershed of Loess Plateau based on deep learning and OBIA

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