Multi-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening

Yancong Wei, Qiangqiang Yuan, Xiangchao Meng, Huanfeng Shen, Liangpei Zhang, Kwok Po NG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Pan-sharpening is a fundamental and significant task in the field of remote sensed imagery fusion, which demands fusion of panchromatic and multi-spectral images with the rich information accurately preserved in both spatial and spectral domains. In this paper, to overcome the drawbacks of traditional pan-sharpening methodologies, we employed the advanced concept of deep learning to propose a Multi-Scale-and-Depth Convolutional Neural Network (MSDCNN) as an end-to-end pan-sharpening model. By the results of a large number of quantitative and visual assessments, the qualities of images fused by the proposed network have been confirmed superior to compared state-of-the-art methods.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3413-3416
Number of pages4
ISBN (Electronic)9781509049516
DOIs
Publication statusPublished - 1 Dec 2017
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/TerritoryUnited States
CityFort Worth
Period23/07/1728/07/17

Scopus Subject Areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • Convolutional neural network
  • Deep learning
  • Pan-sharpening
  • Remote Sensing
  • Residual Learning

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