Performance of pansharpening methods varying with input data formats

A. Arienzo; B. Aiazzi; L. Alparone; A. Garzelli; C. Zoppetti

doi:10.1117/12.2600509

12 September 2021 Performance of pansharpening methods varying with input data formats

A. Arienzo, B. Aiazzi, L. Alparone, A. Garzelli, C. Zoppetti

Proceedings Volume 11862, Image and Signal Processing for Remote Sensing XXVII; 1186208 (2021) https://doi.org/10.1117/12.2600509
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

In this work, we investigate on how the performance of pansharpening methods depends of their input data format, either floating-point, e.g. spectral radiance, or digital numbers (DN) in a packed fixed-point representation. It is theoretically proven and experimentally demonstrated that methods based on multiresolution analysis are unaffected by the data format, which instead is crucial for methods based on component-substitution (CS), unless the intensity component is calculated by means of a multivariate linear regression between the upsampled bands and the lowpass-filtered Pan, as it occurs for the most advanced CS methods. In an experimental setup, WorldView-2 data are either fused in their original 11-bits DN format, or converted to spectral radiance before fusion, by applying the gains and off sets provided in the file header. In the former case, fusion results are converted to spectral radiance before quality is measured. Nine fusion methods have been considered: results exactly match the theoretical investigations. For the majority of CS fusion methods, which do not feature a regression-based intensity calculation, results are better whenever they are obtained from floating-point calibrated data.

Conference Presentation

Citation Download Citation

A. Arienzo, B. Aiazzi, L. Alparone, A. Garzelli, and C. Zoppetti "Performance of pansharpening methods varying with input data formats", Proc. SPIE 11862, Image and Signal Processing for Remote Sensing XXVII, 1186208 (12 September 2021); https://doi.org/10.1117/12.2600509

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