A Pansharpening Method Based on Hybrid-Scale Estimation of Injection Gains

Yan Shi, Aiyong Tan, Na Liu*, Wei Li, Ran Tao, Jocelyn Chanussot

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The injection scheme provides an efficient way for CS- and MRA-based pansharpening approaches. Within this paradigm, the estimation of injection gains is one of the keys to pansharpening outcomes, which has attracted much attention in the community. Most of the existing models are derived from the regression methodology. Hence, the reference is indispensable for the estimation. However, the reference is unavailable in practice, and therefore, the estimation is usually performed at a degraded scale. This article is devoted to the estimation of injection gains without reference. A hybrid-scale (HS) estimation, which involves both the high-resolution and low-resolution data, is proposed, along with three HS models. The proposed method features a context-based and fast implementation with fewer tunable parameters. Experimental results show that the HS models yield more accurate and robust results compared with the typical regression-based models, and they are also competitive with the state-of-the-art approaches.

Original languageEnglish
Article number5400615
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume61
DOIs
Publication statusPublished - 2023

Keywords

  • Hybrid-scale (HS) estimation without reference
  • injection model
  • linear regression
  • pansharpening
  • remote sensing
  • weighted least squares (WLS)

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