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

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.

Download Full-text

Heart Disease Prediction System using Multilayered Feed Forward Neural Network and Back Propagation Neural Network

International Journal of Computer Applications ◽

10.5120/ijca2017914080 ◽

2017 ◽

Vol 166 (7) ◽

pp. 32-36 ◽

Cited By ~ 3

Author(s):

Aditya A. ◽

Sharad N. ◽

Rahul M. ◽

Atharva S. ◽

Shubham A.

Keyword(s):

Neural Network ◽

Heart Disease ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Disease Prediction ◽

Prediction System ◽

Feed Forward Neural Network ◽

Feed Forward

Download Full-text

Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery

Remote Sensing ◽

10.3390/rs12162532 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2532 ◽

Cited By ~ 1

Author(s):

Edoardo Nemni ◽

Joseph Bullock ◽

Samir Belabbes ◽

Lars Bromley

Keyword(s):

Neural Network ◽

Synthetic Aperture Radar ◽

Convolutional Neural Network ◽

Synthetic Aperture ◽

Open Source Data ◽

Wide Range ◽

Radar Imagery ◽

Sar Imagery ◽

Time Required ◽

Aperture Radar

Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring.

Download Full-text

Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

Remote Sensing ◽

10.3390/rs13040604 ◽

2021 ◽

Vol 13 (4) ◽

pp. 604

Author(s):

Donato Amitrano ◽

Gerardo Di Martino ◽

Raffaella Guida ◽

Pasquale Iervolino ◽

Antonio Iodice ◽

...

Keyword(s):

Remote Sensing ◽

Synthetic Aperture Radar ◽

Microwave Remote Sensing ◽

Radar Data ◽

Synthetic Aperture ◽

Special Focus ◽

Arid Environments ◽

Synthetic Aperture Radar Data ◽

Multi Temporal ◽

Aperture Radar

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

Download Full-text