Model-Based Reduced-Rank Pansharpening

Observation of the Earth using satellites mounted with optical sensors is an important application of remote sensing. Owing to physical constraints, multispectral (MS) sensors acquire images of lower spatial resolution than a single-band panchromatic (PAN) sensor that acquires images of the same sce...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2020-04, Vol.17 (4), p.656-660
Main Authors: Palsson, Frosti, Ulfarsson, Magnus O., Sveinsson, Johannes R.
Format: Article
Language:English
Subjects:
Bayesian analysis
Cyclic descent
data fusion
Fuses
Image acquisition
image fusion
Image sensors
Methods
Optical measuring instruments
Optical sensors
Optimization
pansharpening
Probability theory
Remote sensing
Remote sensors
Resolution
Satellite observation
Satellites
Sensor fusion
Sensors
Sharpening
Spatial discrimination
Spatial resolution
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Summary:Observation of the Earth using satellites mounted with optical sensors is an important application of remote sensing. Owing to physical constraints, multispectral (MS) sensors acquire images of lower spatial resolution than a single-band panchromatic (PAN) sensor that acquires images of the same scene. Pansharpening fuses the MS and PAN images to obtain an MS image with the same spatial resolution as the PAN image. In this letter, we propose to expand a method, initially developed for Sentinel-2 single-sensor sharpening, for pansharpening. The expanded method is based on solving a non-convex MS acquisition model using optimization methods based on cyclic decent and manifold optimization. The tuning parameters of the method are chosen using Bayesian optimization with reduced-scale evaluation. The proposed method is compared with a number of established pansharpening methods and is validated using both synthetic and real data sets.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2926681