/DifficultBoundaryRepair

semantic segmentation

Primary LanguagePython

Introduction

This repository is the official implementation of Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation. The main work flow is as follows:

image

This method can be split into two steps: 1. getting coarse segmentation via U-Net; 2. According to the difficult boundary proposals, repairing the boundary in a patch.

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Requirements

  • torch
  • torchvision
  • opencv-python
  • gdal

How to use

  1. train your custom coarse segmentation model

    cd fishpondTrain
python train.py --dataRoot ${your data root} --in_chs 1 --num_classes 2

  2. prediction(coarse segmentation)

    cd fishpondPredict
python predict.py --data_path ${the path of image} --seg_model_path ${coarse segmentation model}

  3. patch repair (training and prediction)

    cd PatchSeg
python train.py --dataRoot ${your data path}
python PatchPredict.py --imgPath entropy_shannon_subset_Feature.tif --CoarseSegPath coarse.tif --modelPath patch.pth --outPath final.tif

Final result

image

Acknowledgement

Thanks for completing this repo with Dr. Yu's kind help.