Open AccessFlood Detection from Satellite Images Based on Deep Convolutional Neural Network and Layered Recurrent Neural Network
Author(s) -
Thirumarai selvi. C,
S Kalieswari.,
R Kuralarasi.,
N Kanimozhi.,
R Kuralarasi.
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e3144.039520
Subject(s) - flooding (psychology) , convolutional neural network , flood myth , computer science , artificial intelligence , feature (linguistics) , satellite , artificial neural network , pattern recognition (psychology) , set (abstract data type) , deep learning , training set , data set , remote sensing , computer vision , geography , engineering , psychology , linguistics , philosophy , archaeology , aerospace engineering , psychotherapist , programming language
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.