Abstract
The spatial resolution of multispectral data can be synthetically improved by exploiting the spatial content of a companion panchromatic image. This process, named pansharpening, is widely employed by data providers to augment the quality of images made available for many applications. The huge demand requires the utilization of efficient fusion algorithms that do not require specific training phases, but rather exploit physical considerations to combine the available data. For this reason, classical model-based approaches are still widely used in practice. We created and assessed a method for improving a widespread approach, based on the generalized Laplacian pyramid decomposition, by combining two different cost-effective upgrades: the estimation of the detail-extraction filter from data and the utilization of an improved injection scheme based on multilinear regression. The proposed method was compared with several existing efficient pansharpening algorithms, employing the most credited performance evaluation protocols. The capability of achieving optimal results in very different scenarios was demonstrated by employing data acquired by the IKONOS and WorldView-3 satellites.
Highlights
Pansharpening [1,2,3,4,5] has generated growing interest in the last years due to the numerous requests for accurate reproductions of the Earth surface, which pushed researchers to enhance the performance of algorithms based on remotely sensed data
The performance analysis can be eased by evaluating the algorithms in pairs, as we show in Figure 6, where the multi-band filter estimation (MBFE)-Gram–Schmidt adaptive (GSA)-multilinear regression (MLR) is compared, in terms of Q2n map, to six other methods based on the generalized Laplacian pyramid (GLP) detail-extraction scheme
A step forward has been made with respect to existing techniques for the efficient combinations of multispectral and panchromatic images acquired by the same satellite
Summary
Pansharpening [1,2,3,4,5] has generated growing interest in the last years due to the numerous requests for accurate reproductions of the Earth surface, which pushed researchers to enhance the performance of algorithms based on remotely sensed data. This setting allows one to obtain a very high quality final product, since the acquisitions can be collected almost contemporaneously from the same platform, thanks to the availability of the two required sensors on many operating satellites.
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