Flood Detection from Satellite Images Based on Deep Convolutional Neural Network and Layered Recurrent Neural Network
Thirumarai Selvi.C1, Kalieswari.S2, Kuralarasi.R3, Kanimozhi.N4, Kanimozhi.M5
1Thirumarai selvi.C*, Department of ECE, Sri Krishna college of Engineering and Technology , Coimbatore, India.
2Kalieswari.S , Department of ECE, Sri Krishna college of Engineering and Technology, Coimbatore, India.
3Kuralarasi.R , Department of ECE, Sri Krishna college of Engineering and Technology, Coimbatore, India.
4Kanimozhi.N, Department of ECE, Sri Krishna college of Engineering and Technology, Coimbatore, India.
5Kuralarasi.R , Department of ECE, Sri Krishna college of Engineering and Technology, Coimbatore, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 2041-2045 | Volume-9 Issue-5, March 2020. | Retrieval Number: E3144039520/2020©BEIESP | DOI: 10.35940/ijitee.E3144.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Satellite images are important for developing and protected environmental resources that can be used for flood detection. The satellite image of before-flooding and after-flooding to be segmented and feature with integration of deeply LRNN and CNN networks for giving high accuracy. It is also important for learning LRNN and CNN is able to find the feature of flooding regions sufficiently and, it will influence the effectiveness of flood relief. The CNNs and LRNNs consists of two set are training set and testing set. The before flooding and after flooding of satellite images to be extract and segment formed by testing and training phase of data patches. All patches are trained by LRNN where changes occur or any misdetection of flooded region to extract accurately without delay. This proposed method obtain accuracy of system is 99% of flood region detections.
Keywords: Satellite Imagery, Flood Detection, Convolutional Neural Network, layered Recurrent Neural Network.
Scope of the Article: Neural Information Processing