Deep convolutional encoder–decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery-Reference-Cited by-同舟云学术

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Deep convolutional encoder–decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery

Published:2024-03-14 Issue:4 Volume:6 Page:408-424
ISSN:2524-4922
Container-title:CCF Transactions on High Performance Computing
language:en
Short-container-title:CCF Trans. HPC

Author:

Zhu Huming^ORCID,Liu Chendi,Li Qiuming,Zhang Lingyun^ORCID,Wang Libing,Li Sifan,Jiao Licheng,Hou Biao

Funder

Key Research and Development Projects of Shaanxi Province

GHFUND A

GHFUND C

Wuhu and Xidian University special fund for industry- university- research cooperation

Cloud Computing Key Laboratory of Gansu Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42514-024-00184-0.pdf

Reference27 articles.

1. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder–decoder architecture for scene segmentation. IEEE Trans. Pattern Anal. Mach. Intell.intell. 39(12), 2481–2495 (2017)

2. Campos, D., Kieu, T., Guo, C., Huang, F., Zheng, K., Yang, B., Jensen, C.S.: Unsupervised time series outlier detection with diversity-driven convolutional ensembles. Proc. VLDB Endow. 15, 611–623 (2021)

3. Carreira, J., Caseiro, R., Batista, J.: Semantic segmentation with second-order pooling. In: European Conference on Computer Vision, pp. 430–443 (2012)

4. Chen, K., Fu, K., Yan, M., et al.: Semantic segmentation of aerial images with shuffling convolutional neural networks. IEEE Geosci. Remote Sens. Lett.geosci. Remote Sens. Lett. 15(2), 173–177 (2018)

5. Cui, B., Jing, W.-P., Huang, L., Li, Z., Yan, Lu.: SANet: a sea–land segmentation network via adaptive multiscale feature learning. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 116–126 (2021)

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