Pansharpening Based on Deconvolution for Multiband Filter Estimation

Vivone, Gemine; Addesso, P.; Restaino, Rocco; Mura, Mauro Dalla; Chanussot, Jocelyn

doi:10.1109/tgrs.2018.2858288

Cited by 53 publications

(27 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, these filters are set exploiting some prior information, as the gains at Nyquist frequency. Obviously, for some reasons (e.g., aging) these values could be slightly wrong and for these reasons some recent research has been focused on this issue, see e.g., [42], [43]. Anyway, the estimation of the convolutional blur B is out-of-scope of this paper, but it can surely deserve future developments to slightly improve the performance in particular at full resolution.…”

Section: A the Spectral Fidelity Termmentioning

confidence: 99%

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

Huang

Deng

et al. 2020

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

Self Cite

View full text Add to dashboard Cite

Pansharpening is a very debated spatio-spectral fusion problem. It refers to the fusion of a high spatial resolution panchromatic (PAN) image with a lower spatial but higher spectral resolution multispectral (LRMS) image in order to obtain an image with high resolution in both the domains. In this paper, we propose a novel variational optimizationbased (VO) approach to address this issue incorporating the outcome of a deep convolutional neural network (DCNN). This solution can take advantages of both the paradigms. On one hand, higher performance can be expected introducing machine learning methods based on the training by examples philosophy into VO approaches. On other hand, the combination of VO techniques with DCNNs can aid the generalization ability of these latter. In particular, we formulate a 2-based proximal deep injection term to evaluate the distance between the DCNN outcome and the desired high spatial resolution multispectral image. This represents the regularization term for our VO model. Furthermore, a new data fitting term measuring the spatial fidelity is proposed. Finally, the proposed convex VO problem is efficiently solved by exploiting the framework of the alternating direction method of multipliers, thus guaranteeing the convergence of the algorithm. Extensive experiments both on simulated and real datasets demonstrate that the proposed approach can outperform state-of-the-art spatio-spectral fusion methods, even showing a significant generalization ability. Please find the project page: https://liangjiandeng.github.io/Projects Res/DMPIF 2020jstars.html.

show abstract

Section: A the Spectral Fidelity Termmentioning

confidence: 99%

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

Huang

Deng

et al. 2020

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The fourth group is the hybrid methods that combine multiple methods described above. The methods proposed by Vivone et al [16], Fei et al [17], and Yin [18] are the ones that combined the component substitution method and the MRA. The combination of the MRA, convolutional neural network (CNN), and sparse modeling was proposed by Wang et al [19], and the combination of the MRA and CNN was proposed by He et al [20].…”

Section: Introductionmentioning

confidence: 99%

Pansharpening by Complementing Compressed Sensing with Spectral Correction

2020

View full text Add to dashboard Cite

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

show abstract

“…Compared with the CS methods, the MRA methods generate less spectral distortion, but usually have a larger computational burden [30]. Recently, several algorithms based on the CS and MRA approaches have been proposed, such as the Sentinel-2A CS and MRA based sharpening algorithm [31], the multiband Filter estimation (MBFE) algorithm [32], and the guided filter PCA (GFPCA) algorithm [33]. Moreover, several intelligent processing-based methods have also been proposed, and examples include deep two-branches convolutional neural network (Two-CNN-Fu) [34], Bidirectional Pyramid Network [35], and 3D-convolutional neural network (3D-CNN) [36].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.

show abstract

Pansharpening Based on Deconvolution for Multiband Filter Estimation

Cited by 53 publications

References 45 publications

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

A New Variational Approach Based on Proximal Deep Injection and Gradient Intensity Similarity for Spatio-Spectral Image Fusion

Pansharpening by Complementing Compressed Sensing with Spectral Correction

Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

Contact Info

Product

Resources

About