Research on automatic identification method of mangrove based on CU-Net model: Taking the Qi’ao Island in Zhuhai City, Guangdong Province as an example

Yu Mingge; Rui Xiaoping; Zou Yarong; Zhang Xi

doi:10.12284/hyxb2023054

Volume 45 Issue 3

Feb. 2023

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Article Navigation > Haiyang Xuebao > 2023 > 45(3): 125-135

Yu Mingge，Rui Xiaoping，Zou Yarong, et al. Research on automatic identification method of mangrove based on CU-Net model: Taking the Qi’ao Island in Zhuhai City, Guangdong Province as an example[J]. Haiyang Xuebao，2023, 45(3)：125–135 doi: 10.12284/hyxb2023054

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Research on automatic identification method of mangrove based on CU-Net model: Taking the Qi’ao Island in Zhuhai City, Guangdong Province as an example

doi: 10.12284/hyxb2023054

Yu Mingge^1
,,
Rui Xiaoping^{1
,
,},
Zou Yarong^{2, 3},
Zhang Xi^{2, 3
,
,}

1.
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
2.
National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
3.
Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China

Received Date: 2022-06-07
Rev Recd Date: 2022-10-26

Available Online: 2022-10-31

Publish Date: 2023-02-01

Abstract

Abstract

Mangroves are important for maintaining biodiversity as well as ecological balance. Therefore, it is necessary to extract mangrove vegetation information efficiently and accurately and to monitor it in real time. A deep learning method for pixel-level accurate extraction of mangroves from high-resolution remote sensing images is presented in this paper. For the problem of low accuracy of mangrove remote sensing classification, CU-Net model for mangrove identification is constructed by introducing CLoss loss function by strengthening image center information and weakening edge information, and adding Dropout and Batch Normalization layers. And a new prediction model is constructed by sliding overlap splicing method, which effectively solves the problem of insufficient edge information and splicing traces in the prediction results. The recognition results of the proposed method are compared with the prediction results of U-Net, SegNet and DenseNet models as well as the traditional SVM and RF methods. The results show that the proposed model has stronger generalization ability and better recognition effect compared with other deep learning models. In the two test areas, the average OA and MIoU reach 94.43% and 88.12%, respectively. The average F1-score in mangrove and ordinary trees reach 95.96% and 90.49%, respectively. The accuracy is significantly higher than that of traditional SVM and RF methods, as well as several other neural networks. The effectiveness of the model in the field of mangrove recognition is verified, which can provide a new idea for the field of high resolution remote sensing mangrove recognition.
- deep learning,
- mangrove identification,
- high-resolution remote-sensing images,
- convolutional neural network

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References(28)

References

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