AUTOMATIC DETECTION OF FLOOD USING REMOTE SENSING DATA
Flood and damage assessment using very Multi-Temporal-Remote Sensing Images (MT-RSI) data
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Flood detection system process like the four different kinds of preprocessing, segmentation, feature extraction and the Contiguous deep Convolutional neural network (CDCNN) has been executed for identifying the flood defected region. CDCNN the implementation of proposed large-scale data sets can automatically pass through the histological characteristics of several layers of neurons, and has the ability to implement the non-linear decision-making functions. This work also investigates and compare with the possible methods for accurately identified by the classification with the proposed CDCNN ...
Flood detection system process like the four different kinds of preprocessing, segmentation, feature extraction and the Contiguous deep Convolutional neural network (CDCNN) has been executed for identifying the flood defected region. CDCNN the implementation of proposed large-scale data sets can automatically pass through the histological characteristics of several layers of neurons, and has the ability to implement the non-linear decision-making functions. This work also investigates and compare with the possible methods for accurately identified by the classification with the proposed CDCNN details of the RSI. The performance analysis of the proposed model is verified in 2017 B mat lab environment. Based on the different features like precision, recall and F-measure accuracy analysis of the proposed system performance simulation system.
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