A Distributed Fusion Framework of Multispectral and Panchromatic Images Based on Residual Network

Yuanyuan Wu,Yuchun Li,Mengxing Huang,Siling Feng,Di Wu

doi:10.3390/rs13132556

Abstract

Remote sensing images have been widely applied in various industries; nevertheless, the resolution of such images is relatively low. Panchromatic sharpening (pan-sharpening) is a research focus in the image fusion domain of remote sensing. Pan-sharpening is used to generate high-resolution multispectral (HRMS) images making full use of low-resolution multispectral (LRMS) images and panchromatic (PAN) images. Traditional pan-sharpening has the problems of spectral distortion, ringing effect, and low resolution. The convolutional neural network (CNN) is gradually applied to pan-sharpening. Aiming at the aforementioned problems, we propose a distributed fusion framework based on residual CNN (RCNN), namely, RDFNet, which realizes the data fusion of three channels. It can make the most of the spectral information and spatial information of LRMS and PAN images. The proposed fusion network employs a distributed fusion architecture to make the best of the fusion outcome of the previous step in the fusion channel, so that the subsequent fusion acquires much more spectral and spatial information. Moreover, two feature extraction channels are used to extract the features of MS and PAN images respectively, using the residual module, and features of different scales are used for the fusion channel. In this way, spectral distortion and spatial information loss are reduced. Employing data from four different satellites to compare the proposed RDFNet, the results of the experiment show that the proposed RDFNet has superior performance in improving spatial resolution and preserving spectral information, and has good robustness and generalization in improving the fusion quality.

Highlights

For a long time, remote sensing images have been widely applied in various industries, such as agricultural yield prediction, plant diseases and pests detection, disaster prediction, geological exploration, national defense, vegetation coverage and land use, environmental change detection, and so on [1,2]
HRHM images are obtained by taking advantage of the redundant and complementary information of high spatial resolution and low spectral resolution images and high spectral resolution and low spatial resolution (LRMS) images
Activated by the advantages of distributed architecture and the residual module, we propose a new three-branch distributed fusion framework of MS and PAN images based on the residual module, RDFNet

Summary

Introduction

Remote sensing images have been widely applied in various industries, such as agricultural yield prediction, plant diseases and pests detection, disaster prediction, geological exploration, national defense, vegetation coverage and land use, environmental change detection, and so on [1,2]. Due to the limitations of satellite sensor technology, it is impossible to obtain images with high spatial resolution and high spectral resolution at the same time. PAN images with high spatial resolution and low spectral resolution and MS images with low spatial resolution and high spectral resolution can be obtained [3]. HRHM images are obtained by taking advantage of the redundant and complementary information of high spatial resolution and low spectral resolution images and high spectral resolution and low spatial resolution (LRMS) images. One of the most used and main technologies is image fusion, which generates a higher quality and more abundant information image by making good. In the process of fusion, the known prior conditions are fully utilized to improve the accuracy of fusion track as much as possible [51]

Methods

Results

Conclusion