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Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
In this repository, we re-factor the skip connections in the raw U-Net and design a Multi-stage Attention ResU-Net (MAResU-Net) for semantic segmentation from fine-resolution remote sensing images.
The detailed results can be seen in the Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images.
The training and testing code can refer to GeoSeg.
The related repositories include:
- MACU-Net->The code to train the network.
- Linear-Attention-Mechanism->The raw inplement of the LAM.
If our code is helpful to you, please cite:
R. Li, S. Zheng, C. Duan, J. Su and C. Zhang. "Multistage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images." in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3063381.
R. Li, C. Duan, S. Zheng, C. Zhang and P. M. Atkinson, "MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2021.3052886.
Acknowlegement:
Thanks very much for the sincere help from Jianlin Su as well as his blog 线性Attention的探索：Attention必须有个Softmax吗？
Requirements：
numpy >= 1.16.5
PyTorch >= 1.3.1
sklearn >= 0.20.4
tqdm >= 4.46.1
imageio >= 2.8.0
Network:
Fig. 1. The structure of (a) the proposed MAResU-Net and (b) the attention block.
Result:
Fig. 2. Visualization of results on the Vaihingen.
Complexity:
Fig. 3. The (a) computation requirement and (b) memory requirement of the raw dot-product attention mechanism and the proposed linear attention mechanism under different input sizes. Please notice that the figure is in log scale.