My final thsis is about satellite segmentaion. I need to extract road、builidings、water or plants from the images.
The dataset must conation images and masks. The dataset is shared below about five kinds of objects.
baidu cloud：https://pan.baidu.com/s/1TcQuMAY2aEiVrFHJd5YIAA code：vfps
google dirve：https://drive.google.com/open?id=1gE6WeoSiXTPEr-mYH8uggXiJEISgBHYZ
I use three models to predict the result. Unet、PSPnet and Segnet. And then I vote for a final result by combining the models' result. Then I do some post_processing for voted images. And then there can be some little improvement.
I just extract road from the models for now. So now I just show the Iou result about the road. I train them for 50 epochs and learning rate is1e-4. Adam optimizer. Here are the result.
| Model_name | Train_miou | Val_miou | Train loss | Loss loss |
|---|---|---|---|---|
| U-net | 0.87640 | 0.74904 | 0.07287 | 0.10107 |
| Seg-net | 0.90533 | 0.78413 | 0.06660 | 0.09993 |
| Psp-net | 0.90404 | 0.75175 | 0.06693 | 0.11606 |
And here is the performance in the different validation area which contaion a large area. As we can see the performance after voting can imporve a lot.
| Model | Valid_1 （city）% | Valid_2 （city）% | Valid_3 （city）% | Valid_4 （town）% | Valid_5 （town）% |
|---|---|---|---|---|---|
| Unet | 0.72285 |
0.74068 |
0.73932 |
0.65743 |
`0.31698 |
| Segnet | 0.75509 |
0.76256 |
0.73416 |
0.65141 |
0.22854 |
| Pspnet | 0.75183 |
0.75508 |
0.71053 |
0.62960 |
0.27243 |
| Vote | 0.77340 | 0.78339* | 0.75360* | 0.67627 | 0.28731 |
image_path = Path('./BDCI2017-seg/CCF-training-Semi')/f'{i}.png'
img_class_path = Path('./BDCI2017-seg/CCF-training-Semi')/ f'{i}_class_vis.png'Note： This step is for the big picture no for less than 1000*1000 shape. If you have hundreds of images, you can just jump to step two.
NOTE: If you want to train on your own dataset, the image name and the mask name must be the same. and the name suffix must be png format.
device = 'cuda'
path = '/home/shiyi/beshe/kinds_dataset/'
learning_rate = 5e-3
num_epochs = 50
num_classes = 5
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
train_dataset = SlippyMapTilesConcatenation(
os.path.join(path, "training", "images"), os.path.join(path, "training", "labels"), transform,debug = False
)
val_dataset = SlippyMapTilesConcatenation(
os.path.join(path, "validation", "images"), os.path.join(path, "validation", "labels"), transform,debug = False
)
The code in predict.py maybe hard to understand. You can just use it. You need use the model you trained in step two.
model = torch.load(f'model/0514pspnet_50_epoch.pth')
# give the picture you want to predict
file_name = f'/home/shiyi/beshe/gaoxin_map/second_dataset/part1_500.png'
# give the name you want to store
save_dir = '0514predict1.png'
# you can just run
python post_deal.py