A Model-Based Method for Pan-Sharpening of Multi-Spectral Images using Sparse Representation

Khateri, Mohammad; Ghassemian, Hassan; Mirzapour, Fardin

doi:10.1109/icsipa45851.2019.8977738

Cited by 3 publications

(1 citation statement)

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“…In [16] sparse representation and low rank regularization is used in a variational optimization setup however this scheme requires empirical parameter setting and suffers from computational cost for high quality pan sharpening. Khatheri et al [17] proposed a model based pan sharpening that uses sparse coefficients with patch dictionary to generate HR-MSI. The fused output preserves image and patch energy ratio as that of LR-MSI.…”

Section: Introductionmentioning

confidence: 99%

Learning deep multiresolution representations for pansharpening

Adeel¹,

Ali²,

Riaz³

et al. 2021

Preprint

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Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening. This paper proposes a pyramid based deep fusion framework that preserves spectral and spatial characteristics at different scales. The spectral information is preserved by passing the corresponding low resolution multispectral image as residual component of the network at each scale. The spatial information is preserved by training the network at each scale with the high frequencies of panchromatic image alongside the corresponding low resolution multispectral image. The parameters of different networks are shared across the pyramid in order to add spatial details consistently across scales. The parameters are also shared across fusion layers within a network at a specific scale. Experiments suggest that the proposed architecture outperforms state of the art pansharpening models. The proposed model, code and dataset is publically available at github 1 .

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Section: Introductionmentioning

confidence: 99%