Improved convolutional neural network in remote sensing image classification

• Published in: Neural computing & applications Vol. 33; no. 14; pp. 8169 - 8180
• Main Author: Xu, Binghui
• Format: Journal Article
• Language: English
• Published: London Springer London 01.07.2021; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s00521-020-04931-6

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.