https://github.com/ZSVOS/FHD

FHD for CD
https://github.com/ZSVOS/FHD

Last synced: about 2 months ago
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FHD for CD

• Host: GitHub
• URL: https://github.com/ZSVOS/FHD
• Owner: ZSVOS
• Created: 2022-05-10T03:30:38.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-21T07:46:40.000Z (almost 2 years ago)
• Last Synced: 2024-07-23T03:38:54.787Z (about 2 months ago)
• Language: Python
• Size: 4.93 MB
• Stars: 18
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - Pei G, Zhang L. Feature Hierarchical Differentiation for Remote Sensing Image Change Detection

README

        
![visitors](https://visitor-badge.glitch.me/badge?page_id=ZSVOS.FHD)

## Overview

> This repository is the official PyTorch implementation for FHD:


> **[Feature Hierarchical Differentiation for Remote Sensing Image Change Detection](https://ieeexplore.ieee.org/document/9837915)**


## Motivation

![FHD](assets/motivation.png)

Figure 1: Comparison of different CD methods. (a) Previous methods. The

deep features of each temporal RS image are extracted by backbone networks,

followed by feature differentiation learning, such as subtraction, concatenation,

fusion, and attention. (b). Proposed FHD. We propose a novel Feature

Hierarchical Differentiation method with TSF and HD modules to select and

fuse critical features. Compared to the previous techniques, the proposed FHD

exhibits higher change detection performance.

## Framework

![FHD](assets/framework.png)

Figure 2: The framework of our proposed FHD.

## Dependencies

To simplify the reproduction steps, we only need to install

```bash

pip install torch==1.7.1 torchvision==0.8.2

pip install mmcv-full==1.3.8 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.7.0/index.html

pip install opencv-python

```

## Dataset

1. Download from [LEVIR](https://justchenhao.github.io/LEVIR/), [DSIFN](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset), [LEVIR+](https://github.com/S2Looking/Dataset), and [S2Looking](https://github.com/S2Looking/Dataset).

2. Crop RS images size of 256 × 256, DSIFN did not crop with 512 × 512.

3. Format as follows:

```

|CD_Dataset

|----train

|---------|A

|---------|B

|---------|label

|----val

...

|----test

...

```

## Training

> Download [MiT-b2 weights](https://drive.google.com/drive/folders/1b7bwrInTW4VLEm27YawHOAMSMikga2Ia) pretrained on ImageNet-1K, and put them in a folder `model_ckpt/`.

```bash

# single GPU (V100 16G)

bash train_eval.sh

```

## Testing

Download [LEVIR, DSIFN, LEVIR+, S2Looking](https://drive.google.com/drive/folders/1am8UiWnvVs72jQo41U0s1pkD4jfO4gsu?usp=sharing), and put it in a folder `model_ckpt/`.

```bash

# single gpu (V100 16G)

bash infer_levir.sh

bash infer_dsifn.sh

bash infer_levir+.sh

bash infer_s2looking.sh

```

## Feature Maps

![FHD](assets/feature_maps.png)

## Quantitative Results

![FHD](assets/results.png)

## Qualitative Results

![FHD](assets/results_pic.png)

## Citation

If you find this useful in your research, please consider citing:

```

@article{pei2022feature,

  title={Feature Hierarchical Differentiation for Remote Sensing Image Change Detection},

  author={Pei, Gensheng and Zhang, Lulu},

  journal={IEEE Geoscience and Remote Sensing Letters},

  year={2022},

  publisher={IEEE}

}

```