SOLC

Remote Sensing Sar-Optical Land-use Classfication Pytorch

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

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

策略	模型	总体性能
epoch=40, batch size=16, Random Flip, lr=1e-3, wd=1e-4	deeplabv3+ (pretrained=False)	oa=0.8096,mIoU=0.4118,kappa=0.7261
epoch=40, batch size=16, Random Flip, lr=1e-3, wd=1e-4	unet (pretrained=False)	oa=0.7846,mIoU=0.3847,kappa=0.6826
epoch=40, batch size=16, Random Flip, lr=1e-3, wd=1e-4	segnet (pretrained=False)	oa=0.7370,mIoU=0.3139,kappa=0.6148
epoch=40, batch size=16, Random Flip, lr=1e-3, wd=1e-4	mcanet (pretrained=False)	oa=0.8179,mIoU=0.4296,kappa=0.7352
epoch=40, batch size=16, Random Flip, lr=1e-3, wd=1e-4	solcv7 (pretrained=False)	oa=0.8222,mIoU=0.4382,kappa=0.7428

模型	farmland	city	village	water	forest	road	others	background(ignored)
deeplabv3+	0.7951	0.9414	0.3928	0.7856	0.9422	0.3504	0.0269	0.0/0.0
unet	0.7970/0.6081	0.5671/0.5174	0.3739/0.2755	0.7517/0.5758	0.9579/0.8418	0.5186/0.2467	0.0216/0.0120	0.0/0.0
segnet	0.7248/0.6122	0.0083/0.0083	0.4314/0.2660	0.8837/0.4845	0.9691/0.8206	0.3984/0.2948	0.14046/0.0249	0.0/0.0
mcanet	0.8320/0.6499	0.8394/0.6575	0.3892/0.3318	0.8177/0.6243	0.9476/0.8724	0.5036/0.2865	0.0169/0.0145	0.0/0.0
solcv7	0.8392/0.6505	0.9051/0.6880	0.3999/0.3337	0.7979/0.6035	0.9425/0.8878	0.5251/0.3250	0.0192/0.0167	0.0/0.0

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

Clone this repo.

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

Install Environments

   $ pip install -r requirements.txt
   $ source activate

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

Training

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

Please see train.py for details.

tensorboard --logdir=<your_log_dir> --bind_all

Test/Predict

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