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

Anusha Nallapareddy, Bharathi Balakrishnan


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.


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

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Amit, S.N.K.B.; Aoki, Y. (2017). Disaster detection from aerial imagery with convolutional neural network, Proc. - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017, 239-245, 2017.

Arvind, S. (2015). Floods in India: Causes and Control, [Online]. Available:, Accesed on 25 February 2019.

Bhatt, C. M.; Rao, G. S. (2016). Ganga floods of 2010 in Uttar Pradesh, north India: a perspective analysis using satellite remote sensing data, Geomatics, Nat. Hazards Risk, 7(2), 747-763, 2016.

Bhatt, C. M.; Rao, G. S.; Farooq, M.; Manjusree, P.; Shukla, A.; Sharma, S. V.S.P.; Kulkarni, S. S.; Begum, A.; Bhanumurthy, V.; Diwakar, P. G.; Dadhwal, V. K. (2017). Satellite-based assessment of the catastrophic Jhelum floods of September 2014, Jammu & Kashmir, India, Geomatics, Nat. Hazards Risk, 8(2), 309-327, 2017.

Bhatt, C. M.; Srinivasa Rao, G., Begum, Asiya; Manjusree, P.; Sharma, S. V.S.P.; Prasanna, L.; Bhanumurthy, V. (2013). Satellite images for extraction of flood disaster footprints and assessing the disaster impact: Brahmaputra floods of June-July 2012, Assam, India, Curr. Sci., 104(12), 1692-1700, 2013.

Brakenridge, R.; Anderson, E.(2006). Modis-Based Flood Detection, Mapping and Measurement: the Potential for Operational Hydrological Applications, Transbound. Floods Reducing Risks Through Flood Manag., 72, 1-12, 2006.

Brezonik, P.; Menken, K. D.; Bauer, M..(2005). Landsat-based remote sensing of lake water quality characteristics, including chlorophyll and colored dissolved organic matter (CDOM), Lake Reserv. Manag., 21(4), 373-382, 2005.

Ceyhun, Ö.; Yalçin, A.(2010). Remote sensing of water depths in shallow waters via artificial neural networks, Estuar. Coast. Shelf Sci., 89(1), 89-96, 2010.

Clement, M. A.; Kilsby, C. G.; Moore, P.(2017). Multi-Temporal SAR Flood Mapping using Change Detection, J. Flood Risk Manag., 11(2), 1-17, 2017.

D'Addabbo, A.; Refice, A.; Pasquariello, G.; Lovergine, F.(2016). SAR/optical data fusion for flood detection, Int. Geosci. Remote Sens. Symp., 7631-7634, 2016.

Faiza, B.; Yuhaniz, S. S.; Mohd Hashim, S. Z.; Kalema, A. K. (2012). Detecting floods using an object based change detection approach, Int. Conf. Comput. Commun. Eng., 44-50, 2012.

Grimaldi, S.; Xu, J.; Li, Y.; Pauwels, V.R.N.; Walker, J. P. (2020). Flood mapping under vegetation using single SAR acquisitions, Remote Sens. Environ., 237, 2020.

Heege, T.; Kiselev, V.; Wettle, M.; Hung, N. N. (2014). Operational multi-sensor monitoring of turbidity for the entire Mekong Delta, Int. J. Remote Sens., 35(8), 2910-2926, 2014.

Hostache, R.; Matgen, P.; Wagner, W. (2012). Change detection approaches for flood extent mapping: How to select the most adequate reference image from online archives?, Int. J. Appl. Earth Obs. Geoinf., 19(1), 205-213. 2012.

Jay, S.; Guillaume, M. (2014). A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data, Remote Sens. Environ., 147, 121-132, 2014.

Jebur, A. A.; Atherton, W.; Al Khaddar, R. M.; Aljanabi, K. R. (2019). Performance analysis of an evolutionary LM algorithm to model the load-settlement response of steel piles embedded in sandy soil, Meas. J. Int. Meas. Confed., 140, 622-635, 2019.

Jensen, J. R. (2013). Remote sensing of the environment: An earth resource perspective, 2/e, Pearson Education India, 2013.

Kaku, K.; Held, A. (2013). Sentinel Asia: A space-based disaster management support system in the Asia-Pacific region, Int. J. Disaster Risk Reduct., 6, 1-17, 2013.

Klemas, V. (2015). Remote Sensing of Floods and Flood-Prone Areas: An Overview, J. Coast. Res., 31(4), 1005-1013, 2015.

Maddox, I. (2014). Three Common Types of Flood Explained, [Online]. Available:, Accesed on 05 March 2019.

Martinis, S.; Kuenzer, C.; Wendleder, A.; Huth, J.; and Twele, A.; Roth, A.; Dech, S. (2015). Comparing four operational SAR-based water and flood detection approaches, Int. J. Remote Sens., 36(13), 3519-3543, 2015.

Matikainen, L.; Hyyppä, J.; Kaartinen, H. (2014). Automatic detection of changes from laser scanner and aerial image data for updating building maps, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., 35, 434-439, 2004.

Nallapareddy, A.; Balakrishnan, B. (2019a). Despeckling of synthetic aperture radar satellite imagery using various filtering techniques, ARPN J. Eng. Appl. Sci., 14(19), 3401-3407, 2019.

Nallapareddy, A.; Balakrishnan, B. (2019b). Flood detection and flood mapping using multitemporal synthetic aperture radar and optical data, Egypt. J. Remote Sens. Sp. Sci., 1-13, 2019.

Nallapareddy, A.; Balakrishnan, B. (2019c). Change Detection and Flood Water Mapping Using Sentinel-1A Synthetic Aperture Radar Images, J. Comput. Theor. Nanosci., 16(8), 3544-3549, 2019.

Rahman, M. R.; Thakur, P. K. (2018). Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India, Egypt. J. Remote Sens. Sp. Sci., 21(1), S37-S41, 2018.

Sanyal, J.; Lu, X. X. (2004). Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review, Nat. Hazards, 33, 283-301, 2004.

Schumann, G. J.P.; Moller, D. K. (2015). Microwave remote sensing of flood inundation, Phys. Chem. Earth, 83-84, 84-95, 2015.

Sitharthan, R. Devabalaji, K. R.; Jees, A. (2017). An Levenberg-Marquardt trained feed-forward back-propagation based intelligent pitch angle controller for wind generation system, Renew. Energy Focus, 22-23, 24-32, 2017.

Sumer, E.; Celebi, F. V. (2006). Detection of tsunami induced changes from high resolution satellite imagery, Proc. - Ocean. 2006 - Asia Pacific, 1-7, 2006.

Tanguy, M.; Chokmani, K.; Bernier, M.; Poulin, J.; Raymond, S. (2017). River flood mapping in urban areas combining Radarsat-2 data and flood return period data, Remote Sens. Environ., 198, 442-459, 2017.

Vu, T. T.; Matsuoka, M.; Yamazaki, F. (2004). LIDAR-based change detection of buildings in dense urban areas, Int. Geosci. Remote Sens. Symp., 5, 3413-3416, 2004.

Yang, S.; Ting, T. O.; Man, K. L.; Guan, S. U. (2013). Investigation of neural networks for function approximation, Procedia Comput. Sci., 17, 586-594, 2013.

Yonghua, S.; Xiaojuan, L.; Huili, G.; Wenji, Z.; Zhaoning, G. (2007). A study on optical and SAR data fusion for extracting flooded area, Int. Geosci. Remote Sens. Symp., 3086-3089, 2007.

[Online]. Asian Disaster Reduction Centre (2011). Natural Disaster Data Book 2011, Available:, Accesed on 18 December 2018.

[Online]. ESA (2020a). User Guides -Sentinel-1 SAR - Naming Conventions - Sentinel Online, Available:, Accesed on 25 February 2019.

[Online]. ESA (2020b). User Guides - Sentinel-1 SAR - Revisit and Coverage - Sentinel Online, Available:, Accesed on 25 February 2019.

[Online]. ESA (2020c). User Guides - Sentinel-1 SAR - SAFE Specification - Sentinel Online, Available: safe-specification, Accesed on 25 February 2019.

[Online]. ESA (2020d). User Guides - Sentinel-1 - Instrument Payload - Sentinel Online, Available:, Accesed on 25 February 2019.

[Online]. ESA (2020e). User Guides - Sentinel-1 SAR - Level-1 - Sentinel Online, Available: level-1, Accesed on 25 February 2019.

[Online]. Firstpost (2017). Uttar Pradesh floods: Death toll rises to 104; over 28 lakh across 24 districts affected, Available: to-103-over-28-lakh-across-24-districts-affected-3992853.html, Accesed on 25 February 2019.

[Online]. Floods in India Essay (2017). Available:, Accesed on 22 February 2019.

[Online]. India-WRIS (2017). India-wris webgis water resource information system of india, Available:, Accesed on 10 March 2019.

[Online]. Hindustan-Times (2017) Two more dams damaged in Gorakhpur, Uttar Pradesh CM surveys flood-affected areas, Available: dams-damaged-in-gorakhpur-uttar-pradesh-cm-surveys-flood-affected-areas/storyaPy1ARk3Jn6S7NQE8CfMwJ. html, Accesed on 10 March 2019.

[Online]. Matlab. (2019). What is a neural network?, Available:, Accesed on 25 February 2019.

[Online]. Water Directors. (2011). Floods Directive (2007/60/EC): Reporting sheets, Series. 1, Issue: February 2011, pp. 1-25, 2011. Available:, Accesed on 26 February 2019.

[Online].World Bank (2017). As Natural Disasters Rise, Countries Call for Action on Resilient Crisis Recovery Planning. Available: disasters-rise-countries-call-for-action-on-resilient-crisis-recovery-planning/, Accesed on 16 June 2019.


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