Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 8; p. 2648
• Main Authors: Aamir, Muhammad, Ali, Tariq, Irfan, Muhammad, Shaf, Ahmad, Azam, Muhammad Zeeshan, Glowacz, Adam, Brumercik, Frantisek, Glowacz, Witold, Alqhtani, Samar, Rahman, Saifur
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09-04-2021; MDPI
• Subjects: Algorithms; convolutional neural network; Cyclones; deep learning; Earthquakes; Ecosystem; Floods; Humans; Image classification; Model accuracy; Model testing; Multilayers; Natural Disasters; natural disasters intensity and classification; Neural networks; Neural Networks, Computer; Support vector machines; Unmanned aerial vehicles; Wildfires; deep learning; natural disasters intensity and classification; convolutional neural network
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21082648

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.