Remote Sensing Image Fusion Algorithm Based on Two-Stream Fusion Network and Residual Channel Attention Mechanism

Mengxing Huang,Siling Feng,Yuanyuan Wu,Shi Liu,Zhenfeng Li,Feng Shu,Di Wu,Chunguo Li

doi:10.1155/2022/8476000

Mengxing Huang, Siling Feng + Show 6 more

Open Access

https://doi.org/10.1155/2022/8476000

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Abstract

A two-stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two-channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen-2 and WorldView-2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.

Highlights

With the widespread application of GIS, the demand for spatial and geographic data in various industries is increasing [1]
Remote sensing image fusion is an algorithm that fuses the panchromatic image with high spatial resolution and the multispectral image with low spatial resolution into the high-resolution multispectral images, shortened to pansharpening [4]
(1) Peak signal-to-noise ratio (PSNR) [49], which is mainly used to evaluate the sensitivity error of fused images’ quality, is an important indicator to measure the difference between two images

Summary

Introduction

With the widespread application of GIS, the demand for spatial and geographic data in various industries is increasing [1]. There are many remote sensing satellites with different functions on various observation platforms outside the earth These satellites can provide different spatial, temporal, and spectral images, that is, remote sensing images. Because of the limitation of satellite sensor, remote sensing satellite can only obtain hyperspectral image and high spatial resolution panchromatic image, respectively. Highresolution multispectral images can calculate the reflection spectrum of each pixel on the earth surface to get a variety of information It can provide help for subsequent remote sensing scene segmentation, classification, and feature extraction such as the survey of forest resources, the ground feature classification, the precision agriculture, and the weather forecast [5].

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Conclusion