Cross-layer detail perception and group attention-guided semantic segmentation network for remote sensing images-Reference-Cited by-同舟云学术

Cross-layer detail perception and group attention-guided semantic segmentation network for remote sensing images

Published:2024 Issue:5 Volume:29 Page:1277-1290
ISSN:1006-8961
Container-title:Journal of Image and Graphics
language:en
Short-container-title:

Author:

Linjuan Li, ,Yun He,Gang Xie,Haoxue Zhang,Yanhong Bai

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Reference21 articles.

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2. Bai H W,Cheng J,Huang X,Liu S Y and Deng C J. 2022. HCANet:a hierarchical context aggregation network for semantic segmentation of high-resolution remote sensing images. IEEE Geoscience and Remote Sensing Letters,19:#6002105[DOI:10.1109/LGRS. 2021.3063799]

3. Bressan P O,Junior J M,Martins J A C,De Melo M J,Gonçalves D N, Freitas D M,Ramos A P M,Furuya M T G,Osco L P,De Andrade Silva J,Luo Z P,Garcia R C,Ma L F,Li J and Gonçalves W N. 2022. Semantic segmentation with labeling uncertainty and class imbalance applied to vegetation mapping. International Journal of Applied Earth Observation and Geoinformation, 108:#102690[DOI:10.1016/J.JAG.2022.102690]

4. Chen L C,Papandreou G,Schroff F and Adam H. 2017. Rethinking atrous convolution for semantic image segmentation[EB/OL].[2023-09-26]. https://arxiv.org/pdf/1706.05587.pdf

5. Chen W T,Ouyang S B,Tong W,Li X J,Zheng X W and Wang L Z. 2022. GCSANet:a global context spatial attention deep learning network for remote sensing scene classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,15:1150-1162[DOI:10.1109/JSTARS.2022.3141826]