Mapping Tea Plantations from Medium-Resolution Remote Sensing Images Using Convolutional Neural Networks and Swin Transformer - Details

author：

Guo, X. (Guo, X..) ^[1] | Liu, J. (Liu, J..) ^[2] | Lu, Y. (Lu, Y..) ^[3] (Scholars：卢毅敏)

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EI Scopus

Abstract：

Tea　is　one　of　the　world＇s　top　three　beverages,　and　the　tea　industry　is　an　important　pillar　of　China＇s　agricultural　economy.　The　sustainable　development　of　the　tea　industry　requires　rapid　and　accurate　tea　plantations　mapping.　In　this　paper,　a　novel　deep　learning　model　(TeaNet)　was　proposed　to　extract　tea　plantations　from　medium-resolution　remote　sensing　images.　The　TeaNet　model,　which　was　designed　as　a　U-shaped　network　structure,　improved　performance　by　coupled　Swin　Transformer　and　convolutional　neural　network　(CNN).　Furthermore,　the　Sentinel　2A　images　of　Wuyishan　City　were　utilized　to　validate　the　proposed　model.　The　results　indicated　that　the　TeaNet　model　shows　a　good　performance,　with　a　recall　of　82.75%,　and　an　F1　score　of　79.48%　which　outperforms　the　UNet　(improved　of　26.58%　and　14.99%).　This　indicates　that　the　TeaNet　model　can　significantly　overcome　the　interference　of　irrelevant　information　and　reduce　the　edge　adhesion　of　tea　plantations,　thereby　identifying　the　planting　areas　of　tea　plantations　and　providing　an　effective　method　for　large-scale　tea　plantation　mapping.　　©　2023　IEEE.

Keyword：

Convolutional Neural Network deep learning remote sensing images Swin Transformer tea plantations mapping

Community：

[ 1 ] [Guo X.]Fuzhou University, The Academy of Digital China (Fujian), Fuzhou, China
[ 2 ] [Liu J.]Fuzhou University, The Academy of Digital China (Fujian), Fuzhou, China
[ 3 ] [Lu Y.]Fuzhou University, The Academy of Digital China (Fujian), Fuzhou, China

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Mapping Tea Plantations from Medium-Resolution Remote Sensing Images Using Convolutional Neural Networks and Swin Transformer
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Source ：

Year： 2023

Language： English

Cited Count：

WoS CC Cited Count： 0

30 Days PV： 3

Affiliated Colleges：

数字中国研究院（福建）本学院/部未明确归属的数据

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