https://github.com/rituyadav92/dausar_supervised_change_detection_floods_igarss2022

Supervised Change Detection for Floods. https://ieeexplore.ieee.org/document/9883132
https://github.com/rituyadav92/dausar_supervised_change_detection_floods_igarss2022

attention-model change-detection sar siamese-network supervised-learning

Last synced: 22 days ago
JSON representation

Supervised Change Detection for Floods. https://ieeexplore.ieee.org/document/9883132

• Host: GitHub
• URL: https://github.com/rituyadav92/dausar_supervised_change_detection_floods_igarss2022
• Owner: RituYadav92
• Created: 2022-08-01T11:59:20.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-01-11T20:21:36.000Z (12 months ago)
• Last Synced: 2024-01-27T20:09:28.803Z (11 months ago)
• Topics: attention-model, change-detection, sar, siamese-network, supervised-learning
• Homepage:
• Size: 99.6 KB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-376/)

[![TensorFlow 1.1](https://img.shields.io/badge/tensorflow-2.9-blue.svg)](https://github.com/tensorflow/tensorflow/releases/tag/v1.15.2)

## Attentive Dual Stream Siamese U-Net for Flood Detection on Multi-Temporal Sentinel-1 Data (IGARSS July 2022)

## Description

Due to climate and land-use change, natural disasters such as flooding have been increasing in recent years. Timely and reliable flood detection and mapping can help emergency response and disaster management. In this work, we propose a flood detection network using bi-temporal SAR acquisitions. The proposed segmentation network has an encoder-decoder architecture with two Siamese encoders for pre and post-flood images. The network's feature maps are fused and enhanced using attention blocks to achieve more accurate detection of the flooded areas. Our proposed network is evaluated on publicly available Sen1Flood11 [1] benchmark dataset. The network outperformed the existing state-of-the-art (uni-temporal) flood detection method by 6% IOU. The experiments highlight that the combination of bi-temporal SAR data with an effective network architecture achieves more accurate flood detection than uni-temporal methods.

We published the dataset here : https://zenodo.org/record/7946594

 

## Paper Citation:

```bash

  @INPROCEEDINGS{9883132,

          author={Yadav, Ritu and Nascetti, Andrea and Ban, Yifang},  

          booktitle={IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},   

          title={Attentive Dual Stream Siamese U-Net for Flood Detection on Multi-Temporal Sentinel-1 Data},   

          year={2022}, volume={}, Number={}, pages={5222-5225},  doi={10.1109/IGARSS46834.2022.9883132}

  }

```

## Dataset Citation:

```bash

Ritu Yadav, “Modified Sen1Floods11 Dataset for Change Detection”. Zenodo, May 17, 2023. doi: 10.5281/zenodo.7946594.

```

## Contact Information/ Corresponding Author

Ritu Yadav (Email: [email protected])