Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kalyan Kumar Jena;Sourav Kumar Bhoi;Soumya Ranjan Nayak;Ranjit Panigrahi;Akash Kumar Bhoi
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引用次数: 3

Abstract

As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.
基于深度卷积网络的卫星云图分类机器智能模型
随着大量卫星围绕地球旋转,通过实时分析卫星图像来观察和确定地球上的变化现象的可能性很大。因此,对卫星图像进行分类对遥感社区预测热带气旋起到了强有力的帮助。在本文中,提出了一种使用深度卷积神经网络(DCNN)的分类方法,该网络包括许多层,通过下采样过程提取特征,用于对卫星云图进行分类。DCNN在云图像上进行了出色的训练,具有令人印象深刻的预测精度。测试图像的交付时间减少,而使用具有大量训练数据集实例的适当深度卷积网络来提高预测精度。卫星图像取自气象和海洋学卫星数据档案中心,该组织负责利用印度及其次大陆的卫星云图。所提出的云图像分类在DCNN框架下显示出94%的预测准确率。
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
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