Survival study on cyclone prediction methods with remote sensing images
Keywords:
Image classification, remote sensing, image intensity, cyclones, wind speed, rainfall intensityAbstract
Image classification has large interest for many decades in the remote sensing communities to reduce injure caused by cyclones. A cyclone is the leading rotating storm that includes the strong wind and rain. It included the number of interrelated features like eye, cyclone pathway, wind speed, generated storm surges, rainfall intensity and so on. Among the features, it is essential one to find in which direction cyclone travels and it influence the areas increasing the damage to life and assets. The cyclone prediction is a key issue where image intensity described the pattern characteristics at various stages. Many existing works have been designed in cyclone prediction for attaining better prediction accuracy. But, it is difficult to enhance the cyclone prediction accuracy with minimum time complexity. In order to address these issues, cyclone prediction can be carried out using deep leaning methods.
Downloads
References
Ailong Ma, Yuting Wan, Yanfei Zhong, Junjue Wang and Liangpei Zhang “SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search”, ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, Volume 172, 2021, Pages 1-18
Pingping Wang, Ping Wang, Cong Wang, Yue Yuan and Di Wang, “A Center Location Algorithm for Tropical Cyclone in Satellite Infrared Images”,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 13, 2020, Pages 2161-2172
Decheng Wang, Xiangning Chen, Mingyong Jiang, Shuhan Du, Bijie Xu and Junda Wang, “ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection”, International Journal of Applied Earth Observation and Geoinformation, Elsevier, Volume 101, September 2021, Pages 1-15
Lei Ding, Jing Zhang and Lorenzo Bruzzone, “Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a Two-Stage Multiscale Training Architecture”, IEEE Transactions on Geoscience and Remote Sensing, Volume 58, Issue 8, August 2020, Pages 5367 – 5376
Muhammad Alam, Jian-Feng Wang, Cong Guangpei, LV Yunrong and Yuanfang Chen, “Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images”, Mobile Networks and Applications, Springer, Volume 26, 2021, Pages 200–215
Chenxiao Zhang, Peng Yue, Deodato Tapetee, Boyi Shangguan and Mi Wanga, Zhaoyan Wub “A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images”, International Journal of Applied Earth Observation and Geoinformation, Volume 88, 2020, Pages 1-13
Yaning Yi, Zhijie Zhang, Wanchang Zhang, Chuanrong Zhang, Weidong Li and Tian Zhao, “Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network”, Remote Sensors, Volume 11, Issue 15, 2019, Pages 1-11
Vinay Ravindra, Sreeja Nag, and Alan Li, “Ensemble-Guided Tropical Cyclone Track Forecasting for Optimal Satellite Remote Sensing”, IEEE Transactions on Geoscience and Remote Sensing, Volume 59, Issue 5, May 2021, Pages 3607 – 3622
Jianxin Cheng, Qiuming Kuang, Chenkai Shen, Jin Liu, Xicheng Tan and Wang Liu, “ResLap: Generating High-Resolution Climate Prediction through Image Super-Resolution”, IEEE Access, Volume 8, February 2020, Pages 9623 – 39634
Snehlata Shakya, Sanjeev Kumar and Mayank Goswami, “Deep Learning Algorithm for Satellite Imaging Based Cyclone Detection”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume 13, 2020, Pages 827-839
Kejie Xu, Hong Huang, Yuan Li, and Guangyao Shi, “Multilayer Feature Fusion Network for Scene Classification in Remote Sensing”, IEEE Geoscience and Remote Sensing Letters, Volume 17, Issue 11, November 2020, Pages 1894 – 1898
Jie Lian, Pingping Dong, Yuping Zhang, Jianguo Pan and Kehao Liu, “A Novel Data-Driven Tropical Cyclone Track Prediction Model Based on CNN and GRU with multi-Dimensional Feature Selection”, IEEE Access, Volume 8, May 2020, Pages 97114 – 97128
Published
How to Cite
Issue
Section
Copyright (c) 2022 International journal of health sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Articles published in the International Journal of Health Sciences (IJHS) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJHS right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.
Articles published in IJHS can be copied, communicated and shared in their published form for non-commercial purposes provided full attribution is given to the author and the journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
This copyright notice applies to articles published in IJHS volumes 4 onwards. Please read about the copyright notices for previous volumes under Journal History.
