BFINet

Official Pytorch Code base for "Irregular agricultural field delineation using a dual-branch architecture from high-resolution remote sensing images"

Project

Introduction

We propose a boundary-field interaction network, namely BFINet, leveraging multitask learning techniques for AF delineation. BFINet comprises two branches: a core branch for AF delineation, and an auxiliary branch for boundary prediction that furnishes fine-grained boundary information to enhance geometric feature learning.

Using the code:

The code is stable while using Python 3.9.0, CUDA >=11.0

• Clone this repository:

git clone https://github.com/NanNanmei/BFINet
cd BFINet

To install all the dependencies using conda or pip:

PyTorch
TensorboardX
OpenCV
numpy
tqdm

Preprocessing

Using the code preprocess.py to obtain boundary maps.

Data Format

Make sure to put the files as the following structure:

inputs
└── <train>
    ├── image
    |   ├── 001.tif
    │   ├── 002.tif
    │   ├── 003.tif
    │   ├── ...
    |
    └── mask
    |   ├── 001.tif
    |   ├── 002.tif
    |   ├── 003.tif
    |   ├── ...
    └── contour
    |   ├── 001.tif
    |   ├── 002.tif
    |   ├── 003.tif
    |   ├── ...
    └── ...

For testing and validation datasets, the same structure as the above.

Training and testing

1. Train the model.

python train.py --train_path ./fields/image --save_path ./model --model_type 'field' 

2. Test the model.

python test.py --model_file ./model/100.pt --save_path ./save --model_type 'field' --test_path ./test_image

3. Accuracy evaluation.

run accuracy_evaluation.py 

A pretrained weight

A pretrained weight of PVT-V2 on the ImageNet dataset is provided: https://drive.google.com/file/d/1uzeVfA4gEQ772vzLntnkqvWePSw84F6y/view?usp=sharing

Acknowledgements:

This code-base uses certain code-blocks and helper functions from BsiNet, SEANet, and HGINet

Citation:

If you find this work useful or interesting, please consider citing the following references.

[1] Zhao H, Long J, Zhang M, et.al. Irregular agricultural field delineation using a dual-branch architecture from high-resolution remote sensing images. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS.
[2] Long J, Li M, Wang X, et.al. Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images. International Journal of Applied Earth Observation and Geoinformation, 2022, 112:102871.
[3] Li M, Long J, Stein A, et.al. sing a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200:24-40.