Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization

Chia Hsiang Wang, Chia Hsiang Lin, José M. Bioucas Dias, Wei Cheng Zheng, Kuo Hsin Tseng

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

In satellite imaging remote sensing, injecting spatial details extracted from a panchromatic image into a multispectral image is referred to as pansharpening, which is ill-posed and requires regularization. Self-similarity, a critical prior knowledge yielding great success in regularizing various imaging inverse problems, has been widely observed in natural images; its formalization is not, however, straightforward. Very recently, we mathematically described the self-similarity pattern as a weighted graph, which can then be transformed into an explicit convex regularizer, that is adopted in our pansharpening criterion design. Most importantly, such convexity allows the adoption of convex optimization theory in solving self-similarity regularized inverse problems with convergence guarantee. One step of our pansharpening algorithm is exactly the proximal operator induced by our new self-similarity regularizer, which is solved by another customized algorithm that is interesting in its own right as could be used as a denoiser. Experiments show promising performance of the proposed method.

Original languageEnglish
Pages3129-3132
Number of pages4
DOIs
StatePublished - 2019
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19

Keywords

  • Convex optimization
  • Denoiser
  • Panchromatic sharpening
  • Proximal operator
  • Self-similarity regularization

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