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Remote Sensing Image Segmentation and Extraction Based on U-Net Convolutional Neural Network Model

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DOI: 10.23977/jipta.2023.060111 | Downloads: 21 | Views: 1008

Author(s)

Chen Xiong 1, Jiaqi Huang 2

Affiliation(s)

1 Xi'an Jiaotong University City College, Xi'an, 710000, China
2 Wuchang University of Technology, Wuhan, 430223, China

Corresponding Author

Chen Xiong

ABSTRACT

Remote sensing images are essential for quickly acquiring large-scale ground information. Segmentation and extraction of high-resolution remote sensing images are widely used in many fields, such as agricultural monitoring, urban and rural planning, and map production and updating. In this paper, a U-Net convolutional neural network model is built on the Tensor Flow framework. A data enhancement strategy is specially designed for the training task of remote sensing image parcel segmentation to enhance the model's generalization ability. The experimental results choose accuracy as the evaluation index, and the final model accuracy can reach 0.9440. The remote sensing image parcel segmentation method proposed in this paper has high training efficiency and is suitable for high-accuracy remote sensing image segmentation and extraction.

KEYWORDS

U-Net; Data Enhancement; Remote sensing images; Image Segmentation

CITE THIS PAPER

Chen Xiong, Jiaqi Huang, Remote Sensing Image Segmentation and Extraction Based on U-Net Convolutional Neural Network Model. Journal of Image Processing Theory and Applications (2023) Vol. 6: 93-99. DOI: http://dx.doi.org/10.23977/jipta.2023.060111.

REFERENCES

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