SKAL

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SKAL for remote sensing scene image classification

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# [Looking Closer at the Scene: Multi-Scale Representation Learning for Remote Sensing Image Scene Classification](https://ieeexplore.ieee.org/document/9298485)
This is the Pytorch implementation of SKAL for remote sensing scene image classification, which is accepted by TNNLS 2020.

# Requirements
Pytorch 1.7, python 3.8, CUDA 11.0

# Usage
Dataset would be arranged as the following form (taking AID as an example):
```plaintext
./datasets/AID/
│
├── images/                  
│   ├── agricultural
│   ├── airplane
│   ├── baseballdiamond
│   └── ...
└── splits
```

Build the training and validation list (Taking AID as an example):
> cd ./datasets  
> python build_list  --data_dir ./AID/images  --out_dir ./AID/splits  --train_ratio 0.5

Train the model at two seperate scales (validate the model every epoch):  
> cd ..  
> firstly, training global_area: python main.py  --dataset selected_dataset  --arch selected_cnn_arch  --mode s1  
> secondly, training local_area: python main.py  --dataset selected_dataset  --arch selected_cnn_arch  --mode s2

# Results
NWPU_RESISC45

|  Method                   | 10\% for training | 20\% for training |
|     ----                  |      ----         |      ----         |
| GoogleNet_{global}        |      77.44        |      83.69        |
| GoogleNet_{global+local}  |      80.28        |      85.34        |
| ResNet18_{global}         |      88.91        |      91.77        |
| ResNet18_{global+local}   |      90.04        |      92.79        |
| GoogleNet_{global}        |      89.40        |      91.93        |
| GoogleNet_{global+local}  |      90.41        |      92.95        |

# Citation
If you want to use the code, please cite: 
> title={Looking Closer at the Scene: Multi-Scale Representation Learning for Remote Sensing Image Scene Classification},  
> author={Q. Wang, W. Huang, Z. Xiong, and X. Li},  
> journal={IEEE Transactions on Neural Networks and Learning Systems},  
> year={2020},  
> }

SKAL

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