Loading [a11y]/accessibility-menu.js
Learning Low-Rank Decomposition for Pan-Sharpening With Spatial-Spectral Offsets | IEEE Journals & Magazine | IEEE Xplore
Scheduled Maintenance: On Monday, 27 January, the IEEE Xplore Author Profile management portal will undergo scheduled maintenance from 9:00-11:00 AM ET (1400-1600 UTC). During this time, access to the portal will be unavailable. We apologize for any inconvenience.

Learning Low-Rank Decomposition for Pan-Sharpening With Spatial-Spectral Offsets


Abstract:

Finding accurate injection components is the key issue in pan-sharpening methods. In this paper, a low-rank pan-sharpening (LRP) model is developed from a new perspective...Show More

Abstract:

Finding accurate injection components is the key issue in pan-sharpening methods. In this paper, a low-rank pan-sharpening (LRP) model is developed from a new perspective of offset learning. Two offsets are defined to represent the spatial and spectral differences between low-resolution multispectral and high-resolution multispectral (HRMS) images, respectively. In order to reduce spatial and spectral distortions, spatial equalization and spectral proportion constraints are designed and cast on the offsets, to develop a spatial and spectral constrained stable low-rank decomposition algorithm via augmented Lagrange multiplier. By fine modeling and heuristic learning, our method can simultaneously reduce spatial and spectral distortions in the fused HRMS images. Moreover, our method can efficiently deal with noises and outliers in source images, for exploring low-rank and sparse characteristics of data. Extensive experiments are taken on several image data sets, and the results demonstrate the efficiency of the proposed LRP.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 29, Issue: 8, August 2018)
Page(s): 3647 - 3657
Date of Publication: 25 August 2017

ISSN Information:

PubMed ID: 28858817

Funding Agency:


References

References is not available for this document.