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Remote Sensing Change Detection via Temporal Feature Interaction and Guided Refinement

Zhenglai Li, Chang Tang, Lizhe Wang, Albert Zomaya

Abstract

Remote sensing change detection (RSCD), which identifies the changed and unchanged pixels from a registered pair of remote sensing images, has enjoyed remarkable success recently. However, locating changed objects with fine structural details is still a challenging problem in RSCD. In this paper, we propose a novel remote sensing change detection network via temporal feature interaction and guided refinement (TFI-GR) to solve this issue. Specifically, unlike previous methods, which just employ one single concatenation or subtraction operation for bi-temporal feature fusion, we design a temporal feature interaction module (TFIM) to enhance interaction between bi-temporal features and capture temporal difference information at diverse feature levels. Afterword, a guided refinement modules (GRM), which aggregates both low- and high-level temporal difference representations to polish the location information of high-level features and filter the background clutters of low-level features, is repeatedly performed. Finally, the multi-level temporal difference features are progressively fused to generate change maps for change detection. To demonstrate the effectiveness of the proposed TFI-GR, comprehensive experiments are performed on three high spatial resolution remote sensing change detection datasets. Experimental results indicate that the proposed method is superior to other state-of-the-art change detection methods.

overview

This figure shows the workflow of our method.

Get Start

1. Download Datasets

Prepare datasets into following structure,

├─Train
    ├─A
    ├─B
    ├─label
    └─list
├─Val
    ├─A
    ├─B
    ├─label
    └─list
├─Test
    ├─A
    ├─B
    ├─label
    └─list

2. Train

sh ./tools/train.sh

3. Test

Pretrained Models

sh ./tools/test.sh

Acknowlogdement

This repository is built under the help of the projects BIT_CD, CDLab, and MobileSal for academic use only.

Citation

@article{li2022cd,
  author={Li, Zhenglai and Tang, Chang and Wang, Lizhe and Zomaya, Albert Y.},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Remote Sensing Change Detection via Temporal Feature Interaction and Guided Refinement}, 
  year={2022},
  volume={60},
  number={},
  pages={1-11},
  doi={10.1109/TGRS.2022.3199502}
}