SGFANet

Implementation of the paper: Learning Sparse Geometric Features for Building Segmentation from Low-Resolution Remote-Sensing Images

Contents

• Overview
• Requirements
• Preparations
• Utilization
• Citation

Overview

Frameworks of the proposed SGFANet. (A) The overall pipeline of the proposed SGFANet, which follows a FPN-like structure, including a bottom-up basic hierarchical feature extractor, a top-down FPN composited by SBSM, GFM and GT, and a light-weight decoder. (B) The sparse boundary fragment sampler module (SBSM), which serves for sampling Tok-K representative feature points about the building boundary (i.e., the edge and corner). K is a hyper-parameter and can be different for edges and corners. (C) The gated fusion module (GFM). It is utilized to calculate the affinity of the selected point-wise features.

Requirements

imagecodecs-lite

opencv-python

opencv-contrib-python

torch==1.7

torchvision

tensorboardX

scikit-image

Pillow

scikit-learn

SciPy

pycococreator

pycocotools

Preparations

Dataset: Baidu Pan Link: https://pan.baidu.com/s/1QcqaCzzK_62nk4IoiApjtA u5wf

Pretrained resnet-50 and resnet-101: Baidu Pan Link: https://pan.baidu.com/s/1I2a0FTtuS6O9p1NqatFKvA 74b5

Download them and make sure to put the dataset and pretrained models as the following structure

Your project
 ├── Data
     ├── GF45
         ├── img
             ├── ...
         ├── gt
             ├── ....
 ├── pretrained_models
     ├── resnet50-deep.pth
     ├── resnet101-deep.pth
 └── Nets
     ├── ......
 └── Loss
     ├── ......
 ......
 

Utilization

An example for training:

CUDA_VISIBLE_DEVICES=0 python main.py --dataset GF4_5 --batch_size 16 --networks Resnet50_SGFANet_edge64_corner16 --epochs 100 --lr 0.001 --random_seed 300

After training, the checkpoint of the project path can be seen, here is an example for evaluating:

CUDA_VISIBLE_DEVICES=0 python evaluate.py --model_path ./GF4_5Resnet50_SGFANet_edge64_corner16-model.ckpt

The segmentation results can be seen in the folder named "GF4_5Resnet50_SGFANet_edge64_corner16"

Citation

@article{liu2023learning, title={Learning Sparse Geometric Features for Building Segmentation from Low-Resolution Remote-Sensing Images}, author={Liu, Zeping and Tang, Hong}, journal={Remote Sensing}, volume={15}, number={7}, pages={1741}, year={2023}, publisher={MDPI} }