Pansharpening is a classical data fusion task that is often necessary when dealing with data sensed through multiresolution acquisition systems. These systems, in fact, provide a single panchromatic band at full spatial resolution coupled with a multispectral lower resolution image of the same scene, which must be fused (pansharpened) to generate a full spatial-spectral resolution datacube. In the last few years, there has been a methodological shift in pansharpening towards the deep learning (DL) paradigm. Most DL solutions proposed thus far use self-supervised learning. Training is carried out on data at downgraded resolution, where ground truth data are also available. Then, the trained network is applied to perform pansharpening on native resolution data. As a consequence, such solutions show good results on low-resolution datasets, but less convincing results on full-resolution data, due to limited generalization ability. In this work, to address this problem, we enrich the training loss function with a perceptual term computed on full-resolution data, obtaining promising experimental results.
A CNN-based pansharpening method with perceptual loss
Vitale S.
2019-01-01
Abstract
Pansharpening is a classical data fusion task that is often necessary when dealing with data sensed through multiresolution acquisition systems. These systems, in fact, provide a single panchromatic band at full spatial resolution coupled with a multispectral lower resolution image of the same scene, which must be fused (pansharpened) to generate a full spatial-spectral resolution datacube. In the last few years, there has been a methodological shift in pansharpening towards the deep learning (DL) paradigm. Most DL solutions proposed thus far use self-supervised learning. Training is carried out on data at downgraded resolution, where ground truth data are also available. Then, the trained network is applied to perform pansharpening on native resolution data. As a consequence, such solutions show good results on low-resolution datasets, but less convincing results on full-resolution data, due to limited generalization ability. In this work, to address this problem, we enrich the training loss function with a perceptual term computed on full-resolution data, obtaining promising experimental results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.