Rice Semantic Segmentation Using Unet-VGG16: A Case Study in Yunlin, Taiwan

Ida Wahyuni, Wei Jen Wang, Deron Liang, Chin Chun Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, Unet-VGG16 semantic segmentation network is proposed to segment rice regions in the aerial images of Yunlin, Taiwan. The experimental results show that different combinations of image bands and different image conditions affect the segmentation accuracy. With R-G-NIR bands as input and bright aerial images as the dataset, the Unet-VGG16 network yielded the best segmentation result, achieving a test accuracy of 0.91.

Original languageEnglish
Title of host publicationISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems
Subtitle of host publication5G Dream to Reality, Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665419512
DOIs
StatePublished - 2021
Event2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021 - Hualien, Taiwan
Duration: 16 Nov 202119 Nov 2021

Publication series

NameISPACS 2021 - International Symposium on Intelligent Signal Processing and Communication Systems: 5G Dream to Reality, Proceeding

Conference

Conference2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021
Country/TerritoryTaiwan
CityHualien
Period16/11/2119/11/21

Keywords

  • Deep Learning
  • Rice
  • Semantic Segmentation
  • Taiwan
  • Unet-VGG16

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