Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms



Flood, Machine learning, Neural network, Satellite, Synthetic Aperture Radar


Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection.

Author Biographies

Anusha Nallapareddy, Sathyabama Institute of Science and Technology

Computer science and Engineering

Bharathi Balakrishnan

Sathyabama Institute of Science and Technology Chennai, 600119, India E-Mail:


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