https://github.com/yochengliu/scasnet
Semantic Labeling in VHR Images via A Self-Cascaded CNN (ISPRS JPRS, IF=6.942)
https://github.com/yochengliu/scasnet
computer-vision context-aware deep-learning multi-context multi-scale remote-sensing semantic-labelling semantic-segmentation
Last synced: 4 months ago
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Semantic Labeling in VHR Images via A Self-Cascaded CNN (ISPRS JPRS, IF=6.942)
- Host: GitHub
- URL: https://github.com/yochengliu/scasnet
- Owner: Yochengliu
- Created: 2017-04-16T11:39:53.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2021-09-30T11:52:30.000Z (over 4 years ago)
- Last Synced: 2025-01-28T03:41:25.415Z (over 1 year ago)
- Topics: computer-vision, context-aware, deep-learning, multi-context, multi-scale, remote-sensing, semantic-labelling, semantic-segmentation
- Homepage: https://www.sciencedirect.com/science/article/pii/S0924271617303854
- Size: 551 KB
- Stars: 25
- Watchers: 4
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
Semantic Labeling in VHR Images via A Self-Cascaded CNN (ScasNet)
===
by [Yongcheng Liu](https://yochengliu.github.io/), [Bin Fan*](http://www.nlpr.ia.ac.cn/fanbin/), [Lingfeng Wang](https://scholar.google.com/citations?user=PaRak2AAAAAJ&hl=en), [Jun Bai](https://www.researchgate.net/profile/Jun_Bai), [Shiming Xiang](https://scholar.google.com/citations?user=0ggsACEAAAAJ&hl=zh-CN), [Chunhong Pan](https://www.researchgate.net/lab/Chunhong-Pan-Lab).
[vai]: ./images/vai.jpg
![vai]
## ScasNet
#### VGG ScasNet
- The encoder is based on VGG-Net variant (Chen et al., 2015), which is to obtain finer feature maps (about 1/8 of input size rather than 1/32).
- On the last layer of encoder, multi-scale contexts are captured by dilated convolutional operations with dilation rates of 24, 18, 12, 6.
- As a trade-off, we only choose three shallow layers for refinement. Moreover, BN layer is not used in VGG ScasNet.
#### ResNet ScasNet
The configuration of ResNet ScasNet is almost the same as VGG ScasNet, except for four aspects:
- the encoder is based on ResNet variant (Zhao et al., 2016)
- four shallow layers are used for refinement
- seven residual correction schemes are designed for feature fusions
- BN layer is used.
## Finetuning
#### For initializing the encoder part in ScasNet
- The encoder in VGG ScasNet is finetuned with [VGG-Net_variant_caffemodel](http://liangchiehchen.com/projects/DeepLabv2_vgg.html)
- The encoder in ResNet ScasNet is finetuned with [ResNet_variant_caffemodel](https://drive.google.com/open?id=0BzaU285cX7TCNVhETE5vVUdMYk0)
## Caffe
- The Caffe we used to train VGG ScasNet is released on [DeepLab_v2](https://bitbucket.org/aquariusjay/deeplab-public-ver2).
- The Caffe we used to train ResNet ScasNet is released on [PSPNet](https://github.com/hszhao/PSPNet).
#### Installation
Please follow the instructions of [Caffe](https://github.com/BVLC/caffe), [DeepLab_v2](https://bitbucket.org/aquariusjay/deeplab-public-ver2) and [PSPNet](https://github.com/hszhao/PSPNet).
The code has been tested successfully on Ubuntu 14.04 with CUDA 8.0.
## References
1. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L., 2015. Semantic image segmentation with deep convolutional nets and fully connected crfs. In: International Conference on Learning Representations.
2. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J., 2016. Pyramid scene parsing network. arXiv preprint arXiv:1612.01105.
## Citation
We would be very glad if ScasNet is helpful for your research, and please consider citing our paper ([arXiv](https://arxiv.org/abs/1807.11236)):
@article{liu2018scasnet,
author = {Yongcheng Liu and
Bin Fan and
Lingfeng Wang and
Jun Bai and
Shiming Xiang and
Chunhong Pan},
title = {Semantic Labeling in Very High Resolution Images via A Self-Cascaded Convolutional Neural Network},
journal = {ISPRS J. Photogram. and Remote Sensing.},
volume = {145},
pages = {78--95},
year = {2018}
}
## Contact
We would be very glad if you have some ideas or questions about ScasNet to share with us, please contact