Pansharpening multispectral remote sensing data by multiplicative joint nonnegative matrix factorization

Karoui, Moussa Sofiane; Djerriri, Khelifa; Boukerch, Issam

doi:10.1080/01431161.2015.1137650

Cited by 10 publications

(2 citation statements)

References 12 publications

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“…MTF GLP HPM [51], CNMF [52], PNN [10], PanNet [12], RSIFNNN [13]. (In github, we show more fusion examples and more state-of-the-art pan-sharpening methods.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

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MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

Yuan,

Sun,

Zhang

2021

Preprint

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Through fusing low resolution Multi-Spectral (MS) images and high resolution PANchromatic (PAN) images, pan-sharpening provides a satisfactory solution for the demands of high resolution multi-spectral images. However, dominant pan-sharpening frameworks simply concatenate the MS stream and the PAN stream once at a specific level. This way of fusion neglects the multi-level spectral-spatial correlation between the two streams, which is vital to improving the fusion performance. In consideration of this, we propose a Multi-level and Enhanced Spectral-Spatial Fusion Network (MESSFN) with the following innovations: First, to fully exploit and strengthen the above correlation, a Hierarchical Multi-level Fusion Architecture (HMFA) is carefully designed. A novel Spectral-Spatial (SS) stream is established to hierarchically derive and fuse the multi-level prior spectral and spatial expertise from the MS stream and the PAN stream. This helps the SS stream master a joint spectral-spatial representation in the hierarchical network for better modeling the fusion relationship. Second, to provide superior expertise, consequently, based on the intrinsic characteristics of the MS image and the PAN image, two feature extraction blocks are specially developed. In the MS stream, a Residual Spectral Attention Block (RSAB) is proposed to mine the potential spectral correlations between different spectra

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“…MTF GLP HPM [51], CNMF [52], PNN [10], PanNet [12], RSIFNNN [13]. (In github, we show more fusion examples and more state-of-the-art pan-sharpening methods.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

“…image. This indicates that the fused images generated by PCA [15], IHS [16], GS [4], and CNMF [52] lose a lot of spectral information and appear serious spectral distortion. The buildings in Fig.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

Yuan,

Sun,

Zhang

2021

Preprint

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A Comprehensive Overview of Satellite Image Fusion: From Classical Model-Based to Cutting-Edge Deep Learning Approaches

Pereira-Sánchez,

Sans,

Navarro

et al. 2024

Unsupervised and Semi-Supervised Learning

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A survey of pansharpening methods with a new band-decoupled variational model

Duran

Buades

Coll

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components. This requirement is achieved by dropping the, commonly used, assumption that relates the spectral and panchromatic modalities by a linear transformation. Instead, a new constraint that preserves the radiometric ratio between the panchromatic and each spectral component is introduced. An exhaustive performance comparison of the proposed fusion method with several classical and state-of-the-art pansharpening techniques illustrates its superiority in preserving spatial details, reducing color distortions, and avoiding the creation of aliasing artifacts.

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Pansharpening multispectral remote sensing data by multiplicative joint nonnegative matrix factorization

Cited by 10 publications

References 12 publications

MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

A Comprehensive Overview of Satellite Image Fusion: From Classical Model-Based to Cutting-Edge Deep Learning Approaches

A survey of pansharpening methods with a new band-decoupled variational model

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