SAR-FloodNet: A Patch-based Convolutional Neural Network for Flood Detection on SAR Images
- 9 May 2022
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC)
Abstract
Flooding and other natural calamities may wreak havoc on people's lives and property. Following the incident, a detailed estimate of the affected area is necessary to undertake rescue operations by deploying an emergency response team. Obtaining an accurate estimate of a flooded region has traditionally required many human resources, and it is time-consuming. In this work, a patch-based Convolutional Neural Network (CNN) model is proposed for quickly detecting floods on remote sensing data. Since SAR - synthetic aperture radar images are acquired with active sensors in any weather condition, these images are considered for flood mapping during both day and night. The SEN 12-FLOOD dataset consisting of SAR images covering the flood events in Western Africa, Iran, and Australia is used to assess the model's performance. The SAR images are divided into small patches, and these patches are fed to the network for training and prediction. The maximum vote is considered for deciding whether the region covered by an image is flooded or not. The proposed SAR-FloodN et outperforms the existing pre-trained models in flood detection, giving an accuracy of 95%.Keywords
Funding Information
- ISRO RESPOND (ISRO/RES/4/685/20-21)
This publication has 26 references indexed in Scilit:
- Extraction of Inundation Areas Due to the July 2018 Western Japan Torrential Rain Event Using Multi-Temporal ALOS-2 ImagesJournal of Disaster Research, 2019
- Data Augmentation Based on Attributed Scattering Centers to Train Robust CNN for SAR ATRIEEE Access, 2019
- Sentinel-1 InSAR Coherence to Detect Floodwater in Urban Areas: Houston and Hurricane Harvey as A Test CaseRemote Sensing, 2019
- Exploiting ConvNet Diversity for Flooding IdentificationIEEE Geoscience and Remote Sensing Letters, 2018
- A new approach for rapid urban flood mapping using ALOS-2/PALSAR-2 in 2015 Kinu River Flood, JapanPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural NetworksIEEE Transactions on Geoscience and Remote Sensing, 2016
- A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary DataIEEE Transactions on Geoscience and Remote Sensing, 2016
- DeepSatPublished by Association for Computing Machinery (ACM) ,2015
- Flooding Water Depth Estimation With High-Resolution SARIEEE Transactions on Geoscience and Remote Sensing, 2014
- Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scatteringInternational Journal of Applied Earth Observation and Geoinformation, 2014