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Open AccessArticle

A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery

by Huasheng Huang 1,2,†, Yubin Lan 1,2,†, Jizhong Deng 1,2,*, Aqing Yang 3, Xiaoling Deng 2,3, Lei Zhang 2,4 and Sheng Wen 2,5
1
College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2
National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China
3
College of Electronic Engineering, South China Agricultural University, Wushan Road, Guangzhou 516042, China
4
College of Agriculture, South China Agricultural University, Wushan Road, Guangzhou 516042, China
5
Engineering Fundamental Teaching and Training Center, South China Agricultural University, Wushan Road, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work and should be considered as co-first authors.
Sensors 2018, 18(7), 2113; https://doi.org/10.3390/s18072113
Received: 13 May 2018 / Revised: 13 June 2018 / Accepted: 27 June 2018 / Published: 1 July 2018
Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery. View Full-Text
Keywords: UAV; remote sensing; weed mapping; Deep Fully Convolutional Network; semantic labeling UAV; remote sensing; weed mapping; Deep Fully Convolutional Network; semantic labeling
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MDPI and ACS Style

Huang, H.; Lan, Y.; Deng, J.; Yang, A.; Deng, X.; Zhang, L.; Wen, S. A Semantic Labeling Approach for Accurate Weed Mapping of High Resolution UAV Imagery. Sensors 2018, 18, 2113.

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