Remote Sensing Image Fusion Using Multi-Scale Convolutional Neural Network

In this paper, a novel remote sensing (RS) image fusion algorithm based on Multi-scale convolutional neural network is proposed. The most important innovation is that the proposed remote sensing image fusion method utilizes a set of convolutional neural networks (CNN) to perform multi-scale image an...

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Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2021-07, Vol.49 (7), p.1677-1687
Main Authors: Shi, Wei, Du, ChaoBen, Gao, BingBing, Yan, JiNing
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Computer vision
Earth and Environmental Science
Earth Sciences
Image analysis
Image processing
Neural networks
Remote sensing
Remote Sensing/Photogrammetry
Research Article
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Summary:In this paper, a novel remote sensing (RS) image fusion algorithm based on Multi-scale convolutional neural network is proposed. The most important innovation is that the proposed remote sensing image fusion method utilizes a set of convolutional neural networks (CNN) to perform multi-scale image analysis on each band of a multispectral image in order to extract the typical characteristics of different band of multispectral images. In addition, to prevent losing the information of the original image, the max-pooling layer of the traditional CNN is replaced with a standard convolutional layer, and the standard convolutional layer has one step size of 2. The RS image fusion results presented in this paper demonstrate that the proposed method is not only competitive with the most advanced methods, but also superior to other classical methods.
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-021-01353-2