基于Unet-VGG16的水稻语义分割:以台湾云林为例

2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI:10.1109/ISPACS51563.2021.9651038

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

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引用次数: 0

摘要

本文提出Unet-VGG16语义分割网络，对台湾云林地区航空影像中的水稻区域进行分割。实验结果表明，不同的图像波段组合和不同的图像条件会影响分割精度。以R-G-NIR波段为输入，以明亮航拍图像为数据集，Unet-VGG16网络分割效果最好，测试精度为0.91。

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

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.

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2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

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