Awesome

SOLC (We have released our complete code !)

Remote Sensing Sar-Optical Land-use Classfication Pytorch

如果该仓库对您有用的话，欢迎star和fork，并提出您的疑问。尽量在issues中提问，有时间一定回复。

Source Dataset

Refer to https://github.com/AmberHen/WHU-OPT-SAR-dataset.

Datasets：Sar and Optical

https://pan.baidu.com/s/1sIGsD3lBEogSCqzbDOaclA password：i51o

Paper Link: MCANet: A joint semantic segmentation framework of optical and SAR images for land use classification

https://www.sciencedirect.com/science/article/pii/S0303243421003457

2022-06-01 News

• Release Crop Code (sar, opt, lbl)
• Release Convert Label Code
• Release Split Code (6:2:2) -> (17640:5880:5880)
• Upload to Server (four GPUs)
• The project should be organized as:

SOLC
├── dataset
|   |whu-opt-sar-dataset-256     //  root
│   ├──   ├── train
│   ├──   │     ├── sar
│   ├──   │     │     ├── NH49E001013_1.tif
│   ├──   │     ├── opt
│   ├──   │     │     ├── NH49E001013_1.tif
│   ├──   │     ├── lbl
│   ├──   │     │     ├── NH49E001013_1.tif
│   ├──   ├── val
│   ├──   │     ├── sar
│   ├──   │     │     ├── NH49E001013_2.tif
│   ├──   │     ├── opt
│   ├──   │     │     ├── NH49E001013_2.tif
│   ├──   │     ├── lbl
│   ├──   │     │     ├── NH49E001013_2.tif
│   ├──   ├── test
│   ├──   │     ├── sar
│   ├──   │     │     ├── NH49E001013_3.tif
│   ├──   │     ├── opt
│   ├──   │     │     ├── NH49E001013_3.tif
│   ├──   │     ├── lbl
│   ├──   │     │     ├── NH49E001013_3.tif
├── libs     //  utils
├── models     //  model
├── tools     //  preprocessing
├── dataset.py
├── class_names.py
├── palette.py 
├── sync_transforms.py 
├── train.py     
├── _test.py / predict.py

2022-06-02 News

• Release Deeplabv3+ Code (sar + opt, 5-channels input)
• Release Learning Strategy Code (step size, gamma)
• Release Training Torch-Parser Code
• Release VGG19 Code (based on official implement) weights

2022-06-03 News

• Release MCANet Code (unofficial implement, based on deeplabv3+)
• Release FCNs Code (FCN8s, 16s and 32s)
• Release Resnet-50 weights , Resnet-101 weights, and Restnet-152 weights Code
• Release Unet Code
• Release Segnet Code
• Release PSPnet Code
• Release SOLC Code (Ours)

2022-06-04 News SOLC (Ours)

• Release SOLC V1 Code (based on RGB-D and dual-resnet 50)
• Release SOLC V2 Code (based on dual-stream deeplabv3+)
• Release Deeplabv3+ Performance (Training 7 hours)
• Release Train Code
• Release Predict/Test Code

2022-06-05 News SOLC (Ours)

• Release SOLC V1 Code Performance
• Release SOLC V2 Code Performance
• Release SOLC V3 Code (based on dual-stream deeplabv3+ and SAGate)
• Release SOLC V4 Code (based on dual2one-stream deeplabv3+)
• Release SOLC V5 Code (based on dual2one-stream deeplabv3+, SAGate and RFB)
• Release SOLC V6 Code (based on dual2one-stream deeplabv3+ and Two Enhanced Module)

2022-06-06 News SOLC (Ours)

• Release SOLC V7 Code (based on dual-stream deeplabv3+, SAGate and ERFB) (successful version, Congratulations!)
• Retrain Unet, Segnet, and MCANet
• Retest the performance
• Release Our SOLC V7 weights solcv7: baiduyun password：solc
• Release Other model weights others: baiduyun password：solc

2022-06-07 News

• Release 像素统计

[0, '背景', 11824154.0, 1.0] (忽略)
[1, '农田', 708762053.0, 0.016682825992096393]
[2, '城市', 96237141.0, 0.12286476797975535]
[3, '村庄', 119738993.0, 0.09874940237721894]
[4, '水体', 292127191.0, 0.04047604729817842]
[5, '森林', 774385740.0, 0.015269075073618998]
[6, '道路', 19661970.0, 0.6013717852280317]
[7, '其他', 35164414.0, 0.3362534066400197]
weight in criterion: [1.0, 0.016682825992096393, 0.12286476797975535, 0.09874940237721894, 0.04047604729817842, 0.015269075073618998, 0.6013717852280317, 0.3362534066400197]

2022-06-08 News

• Release More Results
• Release Plot Code

SOLC V7 framework

Other stragety

• 设置合适的空洞卷积膨胀率atrous_rates
• 余弦退火重启动学习率策略warm up
• 使用更多的数据增强
• 使用更强的损失函数（focal loss）或者为类别赋予权重（见tools/class_weight.py） 来解决类别不平衡问题

Performance

左侧为各类别的平均精度OA，右侧为各类别的平均mIou

Note: 0->background，10->farmland，20->city，30->village，40->water，50->forest，60->road，70->others

Installation

1. Clone this repo.

$ git clone https://github.com/yisun98/SOLC.git
$ cd SOLC

2. Install Environments

   $ pip install -r requirements.txt
   $ source activate

3. Dataset

   $ python tools/crop_sar.py
   $ python tools/crop_opt.py
   $ python tools/convert_lbl.py
   $ python tools/crop_lbl.py
   $ python tools/split_data.py

4. Training

nohup python train.py >> train_<model_name>.out 2>&1 &

Please see train.py for details.

tensorboard --logdir=<your_log_dir> --bind_all 

5. Test/Predict

nohup python train.py --model solcv7 --num_classes 8 >> train_<model_name>.out 2>&1 &

Please see train.py for details.

python predict.py --model solcv7 --model-path <model_path>

Result

More results please see here password：solc.

classification_25_21

classification_82_10

classification_94_2

classification_137_15

Reference

Citation (同等贡献度，排名不分先后)

If you have any questions, welcome to contact us !

Contributors 1: yisun98 - 1957240687@qq.com
Contributors 2: yiruzzz - 992371522@qq.com
Contributors 3: Bismarckwzc - 1558908027@qq.com
Contributors 4: 917596622 - 917596622@qq.com

If the project is helpful to you, please consider citing us.

@misc{SOLC2022,
  author = {Y. Sun, Y. Zhao, Z. Wang, Y. Fan},
  title = {SOLC},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/yisun98/SOLC}},
}