Dual-Collaborative Fusion Model for Multispectral and Panchromatic Image Fusion

Xing, Yinghui; Yang, Shuyuan; Feng, Zhixi; Jiao, Licheng

doi:10.1109/tgrs.2020.3036625

Cited by 17 publications

(8 citation statements)

References 46 publications

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“…However, simply stacking pre-interpolated MS with Pan as the input of network not only ignores individual features but also raises extra computational burden. Hence, instead of treating the two modalities equally, multi-branch networks apply different sub-networks to separately extract the modality-specific features (Shao and Cai, 2018;Zhang et al, 2019c;Liu et al, 2020b;Chen et al, 2021;Zhang and Ma, 2021;Xing et al, 2020;Yang et al, 2022b).…”

Section: Homogeneous Fusionmentioning

confidence: 99%

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

Li¹,

Hong²,

Gao³

et al. 2022

Preprint

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Section: Homogeneous Fusionmentioning

confidence: 99%

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

Li¹,

Hong²,

Gao³

et al. 2022

Preprint

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“…The current mainstream DL methods are processed in blocks in pansharpening, such as 256 × 256. In a published paper [29], the processing time 1 For simplicity, we use pansharpening for both MS and HS pansharpening.…”

Section: Introductionmentioning

confidence: 99%

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Xie

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper proposes an efficient and high-fidelity panchromatic (PAN) spectral decomposition method based on smoothing filtering for multispectral (MS) and hyperspectral (HS) image pansharpening. The proposed method assumes that the high-frequency spatial details at the same scale are the same for different spectral images captured by the satellite at the same time. When the PAN image is pre-filtered and downsampled to the MS scale, it will have the same high-frequency spatial detail as the MS image, with only low-frequency spectral differences. Pre-filtering for anti-aliasing when downsampling. Then the spectral decomposition coefficients from down-sampled PAN image to MS image can be calculated on MS scale. The lowfrequency spectral information of an image at different scales is the same, the spectral decomposition coefficients on the MS scale can be up-sampled to the PAN scale, and the original PAN image can be spectrally decomposed to obtain the sharpened image. The proposed method only decomposes the low-frequency spectrum, and the high-frequency spatial details are the same as the original PAN image, the spatial detail is well preserved. This paper verifies the effect of the proposed method on MS and HS image sharpening through experiments, and the results show that the proposed method is better than the comparison method. This paper also controls other variables to compare with the HPM method. The results show that the hybrid quality with no reference (HQN R) and spatial distortion index (Ds) of the proposed method are better than the HPM method.

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“…Xiong et al 19 used the depth CNN to learn the spectral information of MS images and applied the spectral angle to control spectral loss. Xu et al proposed a model-based deep PAN sharpening method 20 and Xing et al 21 proposed a double collaborative fusion model.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al. proposed a model-based deep PAN sharpening method 20 and Xing et al 21 . proposed a double collaborative fusion model.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing image fusion based on NSST and parameter adaptive PCNN

Pan

Liu

Wang

et al. 2022

J. Appl. Rem. Sens.

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.Remote sensing image fusion aims to enhance the spatial resolution of multispectral (MS) images by extracting spatial detail information from panchromatic (PAN) images. To preserve the spectral information of MS images and enhance the spatial resolution, an innovative remote sensing image fusion method is proposed here. The proposed method is based on an image matting model, the non-subsampled shear wave transform (NSST) and a parameter adaptive pulse coupled neural network (PA-PCNN). In the proposed method, an intensity component, I, is first obtained by adaptively weighted averaging over each band of the MS image. An image matting model is then applied. There, I is used as the α channel to estimate the foreground color, F, and the background color B. Then, the I and PAN images are decomposed into low and high-frequency coefficients by NSST. Two fusion rules are proposed here and applied to fuse the low and high-frequency coefficients, respectively. The fused low and high-frequency coefficients are inversely transformed to get a fused image, which is used as the α channel in the reconstruction of the image. Finally, the F, B and fusion images are combined to reconstruct the final high-resolution fused image. The experimental results show that the proposed approach achieves superior results in both subjective visual effects and objective assessment. It can enhance the spatial resolution and effectively retaining the spectral information of MS images.

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Dual-Collaborative Fusion Model for Multispectral and Panchromatic Image Fusion

Cited by 17 publications

References 46 publications

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

Smoothing Filter-Based Panchromatic Spectral Decomposition for Multispectral and Hyperspectral Image Pansharpening

Remote sensing image fusion based on NSST and parameter adaptive PCNN

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