Incorporating convolutional and transformer architectures to enhance semantic segmentation of fine-resolution urban images-Reference-Cited by-同舟云学术

Incorporating convolutional and transformer architectures to enhance semantic segmentation of fine-resolution urban images

Published:2024-06-04 Issue:1 Volume:57 Page:
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Yu Xizi¹,Li Shuang²³⁴,Zhang Yu²³⁴

Affiliation:

1. MOE Key Laboratory of Western China’s Environmental Systems, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China

2. Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, China

3. Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou, China

4. Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, China

Funder

Key-Area Research and Development Program of Guangdong Province

Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning

The Science and Technology Foundation of Guangzhou Urban Planning & Design Survey Research Institute

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2024.2361768

Reference72 articles.

1. Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images

2. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

3. Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation

4. Caye Daudt, R., Le Saux, B., & Boulch, A. (2018). Fully convolutional siamese networks for change detection. 2018 25th IEEE International Conference on Image Processing (ICIP) Presented at the 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 4063–4067). https://doi.org/10.1109/ICIP.2018.8451652

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. MBT-UNet: Multi-Branch Transform Combined with UNet for Semantic Segmentation of Remote Sensing Images;Remote Sensing;2024-07-29