Classification of tea garden based on multi-source high-resolution satellite images using multi-dimensional convolutional neural network

doi:10.6046/zrzyyg.2021202

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Abstract

The terrain conditions and tea plantation structure of Wuyishan City are complex, with cloudy and rainy weather, so it is difficult to obtain satellite images here. To address the problem of difficult extraction of tea gardens from a single image source, we investigated the spectral information of Sentinel-2 images and the texture features of Google images in Xintian Town, Wuyishan City, coupled with which a tea garden classification method based on multi-source high-resolution satellite images and multidimensional convolutional neural networks (MM-CNN) was established. In this method, tea gardens and suspected tea gardens were extracted, respectively, with two models developed with images with different spatial resolutions, based on one- and two-dimensional CNN. Results obtained with the two CNN models were combined, and the high-accuracy distribution of tea gardens in the study area was generated in a relatively economical and efficient way. The results showed that the spatial distribution accuracy of the tea gardens identified by MM-CNN is better than that of the single image source method. The MM-CNN method is highly universal and robust and provides a reference method for efficiently monitoring the distribution of tea gardens in large-scale hilly areas of South China.

Keywords Wuyishan City tea garden convolutional neural network semantic segmentation U-Net 1D-CNN Sentinel-2 Google image

ZTFLH:

TP79

Corresponding Authors: YANG Zeyu E-mail: 85832679@qq.com;yangzywhu@sina.cn

Issue Date: 20 June 2022

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	Kuo LIAO
	Lei NIE
	Zeyu YANG
	Hongyan ZHANG
	Yanjie WANG
	Jida PENG
	Haofei DANG
	Wei LENG

Cite this article:

Kuo LIAO,Lei NIE,Zeyu YANG, et al. Classification of tea garden based on multi-source high-resolution satellite images using multi-dimensional convolutional neural network[J]. Remote Sensing for Natural Resources, 2022, 34(2): 152-161.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021202 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/152

Fig.1 Integrated high spatial remote sensing images in the study area, field survey polygons and ground truth label making areas

Fig.2 Spectral curves of different surface features

Fig.3 Time variation curves of spectral reflectance in different surface features

Fig.4 Typical texture characteristics of tea gardens in the study area

Fig.5 1D-CNN network structure

Fig.6 U-Net network structure

Fig.7 Framework of tea garden distribution extraction in this study

Fig.8 Classification results comparison before and after post-processing

Tab.1 Tea garden classification results from different classification methods

Tab.2 Accuracy results of different tea garden classification methods

Fig.9 Classification result of tea gardens in the study area

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