DEEP CONVOLUTIONAL SPARSE CODING NETWORK FOR PANSHARPENING WITH GUIDANCE OF SIDE INFORMATION

Shuang Xu, Jiangshe Zhang, Kai Sun, Zixiang Zhao, Lu Huang, Junmin Liu, Chunxia Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelated feature map, where the former one is regularized by the side information from panchromatic images. With the principle of algorithm unrolling techniques, the proposed model is generalized as a deep neural network, called as SCSC pansharpening neural network (SCSC-PNN). Compared with 13 classic and state-of-the-art methods on three satellites, the numerical experiments show that SCSC-PNN is superior to others. The codes are available at https://github.com/xsxjtu/SCSC-PNN.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo, ICME 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665438643
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

Keywords

  • Pan-sharpening
  • algorithm unrolling
  • convolutional sparse coding
  • image fusion

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