Abstract
Pansharpening is the process of merging the spectral resolution of a multi-band remote-sensing image with the spatial resolution of a co-registered single-band panchromatic observation of the same scene. Conceived and contextualized over 30 years ago, panharpening methods have progressively become more and more sophisticated, but simultaneously they have started producing fewer and fewer reproducible results. Their recent proliferation is most likely due to the lack of standardized assessment procedures and especially to the use of non-reproducible results for benchmarking. In this paper, we focus on the reproducibility of results and propose a modified version of the popular additive wavelet luminance proportional (AWLP) method, which exhibits all the features necessary to become the ideal benchmark for pansharpening: high performance, fast algorithm, absence of any manual optimization, reproducible results for any dataset and landscape, thanks to: (i) spatial analysis filter matching the modulation transfer function (MTF) of the instrument; (ii) spectral transformation implicitly accounting for the spectral responsivity functions (SRF) of the multispectral scanner; (iii) multiplicative detail-injection model with correction of the path-radiance term introduced by the atmosphere. The revisited AWLP has been comparatively evaluated with some of the high performing methods in the literature, on three different datasets from different instruments, with both full-scale and reduced-scale assessments, and achieves the first place, on average, in the ranking of methods providing reproducible results.
Highlights
For several decades, satellite systems for Earth observation (EO) have acquired a huge number of images of crucial importance for many human tasks
Toulouse dataset: An IKONOS image composed by 512×512 MS pixels and 2048×2048 panchromatic pixels has been acquired over the urban area of Toulouse, France, in June 2000
We pointed out the need for a fast and high performing pansharpening method providing reproducible results on any dataset of any size
Summary
Satellite systems for Earth observation (EO) have acquired a huge number of images of crucial importance for many human tasks These data are used for visual interpretation or as intermediate products for subsequent processing and information extraction. Speaking, pansharpening increases the spatial resolution of the MS image paying it by the introduction of spectral distortion It is often useful for visual or automated analysis tasks. Super-resolution, or, generally speaking, optimization based variational methods, either model-based [10,11] or not [12], usually suffer from higher computational burden with respect to traditional approaches.
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