Abstract
In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. The article aims to fully focus on cloud computing technology and convolutional neural network technology to carry out specific application research, on this basis, to obtain a more convenient, accurate and efficient remote sensing image classification method, and to prove the feasibility of this method. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing and then compares traditional methods from the selection of feature extraction methods and classifiers. The remote sensing image classification method is described, and finally, the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the accuracy of the traditional method is below 80%, while the feature extraction of the pre-trained network has an accuracy of more than 90% on the UCMERCE data; it combines the three pre-trained networks of ResNet-50, ResNet-101 and ResNet-152, an increase of more than 2.5%.
Highlights
In recent years, the instruments and technologies that can be used for earth observation have been developed rapidly
The remote sensing image classification method is described, and the convolutional neural network model that needs to be applied in this research is explained
The experimental results in this paper show that the remote sensing image big data classification method based on cloud computing and convolutional neural network has more than a little improvement in accuracy than the traditional remote sensing image classification method, and the accuracy rate has reached 86.3%
Summary
The instruments and technologies that can be used for earth observation (including multi-spectrometer, hyper-spectrometer, synthetic aperture radar, etc.) have been developed rapidly. Spectral analysis and different time analysis of these high-quality remote sensing images provide important information support for the introduction of intelligent ground observation. High-quality, high-performance remote sensing images indicate a higher demand for related research. Remote sensing image classification has always been an active research topic in the field of remote sensing image analysis. The classification of remote sensing images requires that images be classified into a set of meaningful categories based on the content of remote sensing images. Scene classification of telemetry images plays an important role in many applications. In the research of natural disaster detection, the determination of coverage area, spatial object detection, geographic image extraction, vegetation map, environmental monitoring and urban planning, is an important step in the sorting of remote sensing scene images
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
