Boosting Remote Sensing Change Detection via Hard Region Mining (Under Review)

This repository contains simple python implementation of our paper HRMNet.

1. Overview

A framework of the proposed AR-CDNet. Initially, the bi-temporal images pass through a shared feature extractor to obtain bi-temporal features, and then multi-level temporal difference features are obtained through the TDE. CKRMs fully explore the multi-level temporal difference knowledge to enhance the feature capabilities. The OHRE branch estimates pixel-wise hard samples corresponding of changed and unchanged regions, supervised by the diversity between predicted change maps and corresponding ground truth in the training process. Finally, the multi-level temporal difference features and hard region aware feature obtained from the OHRE branch are aggregated to generate the final change maps.

2. Usage

• Prepare the data:

  • Download datasets LEVIR, BCDD, and MCD.
  • Crop LEVIR, BCDD, and MCD datasets into 512x512 patches.
  • The pre-processed BCDD dataset can be obtained from BCDD_512x512.
  • For MCD dataset, we provide ./datasets/split_MCD.py to Crop MCD into 512x512 patches.
  • Prepare datasets into the following structure and set their path in train.py and test.py

  ├─Train
      ├─A        ...jpg/png
      ├─B        ...jpg/png
      ├─label    ...jpg/png
      └─list     ...txt
  ├─Val
      ├─A
      ├─B
      ├─label
      └─list
  ├─Test
      ├─A
      ├─B
      ├─label
      └─list
  

  • Generate list file as ls -R ./label/* > test.txt or using ./datasets/data_inf.py

• Prerequisites for Python:

  • Creating a virtual environment in the terminal: conda create -n HRMNet python=3.8
  • Installing necessary packages: pip install -r requirements.txt

• Train/Test

  • sh train.sh
  • sh test.sh