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Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2022

CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution

Résumé

Traditional hyperspectral (HS) pansharpening aims at fusing a HS image with its panchromatic (PAN) counterpart, to bring the spatial resolution of the HS image to that of the PAN image. However, in many practical applications, arbitrary resolution HS (ARHS) pansharpening is required, where the HS and PAN images need to be integrated to generate a pansharpened HS image with arbitrary resolution (usually higher than that of the PAN image). Such an innovative task brings forth new challenges for the pansharpening technique, mainly including how to reconstruct HS images beyond the training scale and how to guarantee spectral fidelity at any spatial resolutions. To tackle the challenges, we present a novel convolutional neural network (CNN)-based method for ARHS pansharpening called ARHS-CNN. It is based on a two-step relay optimization process, which is associated with a multilevel enhancement subnetwork and a rescaling subnetwork. With a careful design following the thread, our ARHS-CNN is able to pansharpen HS images to any spatial resolutions using just a single CNN model trained on a limited number of scales while meantime to keep spectral fidelity at those resolutions, which wins an obvious advantage over traditional pansharpening methods. Experimental results obtained on several datasets verify the excellent performance of our ARHS-CNN method.
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Dates et versions

hal-03932762 , version 1 (10-01-2023)

Identifiants

Citer

Lin He, Jiawei Zhu, Jun Li, Antonio Plaza, Jocelyn Chanussot, et al.. CNN-Based Hyperspectral Pansharpening With Arbitrary Resolution. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp.5518821. ⟨10.1109/TGRS.2021.3132997⟩. ⟨hal-03932762⟩
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