Abstract
Image segmentation is an important basic link of remote sensing interpretation. High-resolution remote sensing images contain complex object information. The application of traditional segmentation methods is greatly restricted. In this paper, a remote sensing semantic segmentation algorithm is proposed based on ResU-Net combined with Atrous convolution. The traditional U-Net semantic segmentation network was improved as the backbone network, and the residual convolution unit was used to replace the original U-Net convolution unit to increase the depth of the network and avoid the disappearance of gradients. To detect more feature information, a multi-branch hole convolution module was added between the encoding and decoding modules to extract semantic features, and the expansion rate of the hole convolution was modified to make the network have a better effect on the small target category segmentation. Finally, the remote sensing image was classified by pixel to output the remote sensing image semantic segmentation result. The experimental results show that the accuracy and interaction ratio of the proposed algorithm in the ISPRS Vaihingen dataset are improved, which verifies its effectiveness.
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Chen, S., Zuo, Q., Wang, Z. (2021). Semantic Segmentation of High Resolution Remote Sensing Images Based on Improved ResU-Net. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_23
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DOI: https://doi.org/10.1007/978-981-16-5940-9_23
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