satellite_segmentaion

Introduction:

My final thsis is about satellite segmentaion. I need to extract road、builidings、water or plants from the images.

About the dataset:

The dataset must conation images and masks. The dataset is shared below about five kinds of objects.

About the mothod:

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.

About the Result:

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.

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.

If you want use this code. you can just clone it . I will add the requirments.txt soon.

First step: prepare you own dataset:

You need to give the right path for images and masks in dataset_processing.py

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'

Second step: train

give the path of dataset and alter the params you want to use.

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
)

Third step: Prediction

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' 

Fourth Step: vote and post_processing

The post_processing progress can be interesting. You can different kinds of combanation.

# you can just run 
python post_deal.py