Improved convolutional neural network in remote sensing image classification

The classification of land cover is the first step in the analysis and application of remote sensing data in land resources. How to solve the multi-category image recognition and meet certain precision is a key issue in remote sensing image research, which has very important theoretical significance...

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Vydáno v:Neural computing & applications Ročník 33; číslo 14; s. 8169 - 8180
Hlavní autor: Xu, Binghui
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.07.2021
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Geomorphology
Image classification
Image Processing and Computer Vision
Land cover
Neural networks
Object recognition
Probability and Statistics in Computer Science
Remote sensing
S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
Special Issue on Intelligent Computing Methodologies in Machine learning for IoT Applications
Weather
Remote sensing image
Image recognition
Convolutional neural network
Algorithm improvement
Image classification
ISSN:0941-0643, 1433-3058
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Shrnutí:The classification of land cover is the first step in the analysis and application of remote sensing data in land resources. How to solve the multi-category image recognition and meet certain precision is a key issue in remote sensing image research, which has very important theoretical significance and practical application value. In this study, the algorithm is improved on the basis of convolutional neural network, and experiments are carried out on multi-source remote sensing images with different geomorphologies taken under three different weather conditions to verify the effectiveness and scalability of the improved convolutional neural network. The research results show that the improved algorithm proposed in this paper has certain results in remote sensing image classification and can provide theoretical reference for subsequent related research.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-04931-6