Multiresolution Analysis Pansharpening Based on Variation Factor for Multispectral and Panchromatic Images From Different Times

Wang, Peng; Yao, Hongyu; Huang, Bo; Leung, Henry; Liu, Pengfei

doi:10.1109/tgrs.2023.3252001

Cited by 10 publications

(4 citation statements)

References 63 publications

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“…Figures 7 and 8 show the fusion results of the Washington dataset under different algorithms. To show the spatial details of the images, [40,30,5] was chosen as the RGB band for the pseudo-color images. Figure 7a,b represents the multispectral image after the spectral downsampling process and the hyperspectral image with spatial downsampling, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Pixel-level fusion methods are classified as component replacement (CS) [2][3][4], multiresolution analysis (MRA) [5,6], hybrid methods [7,8], and model-based methods. These methods include intensity hue saturation (IHS) [9,10] methods, the Brovey transform [11], principal component analysis (PCA) [12], Gram-Schmidt (GS) [13] orthogonalization, the Laplace pyramid [14], the curvelet transform [15], and sparse matrix decomposition [16].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition

Sun,

Bao,

et al. 2023

Remote Sensing

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This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution. This algorithm seamlessly integrates graph regularization and tensor ring decomposition, presenting an innovative fusion model that effectively leverages the spatial structure and spectral information inherent in hyperspectral images. At the core of the algorithm lies an iterative optimization process embedded within the objective function. This iterative process incrementally refines latent feature representations. It incorporates spatial smoothness constraints and graph regularization terms to enhance the quality of super-resolution reconstruction and preserve image features. Specifically, low-resolution hyperspectral images (HSIs) and high-resolution multispectral images (MSIs) are obtained through spatial and spectral downsampling, which are then treated as nodes in a constructed graph, efficiently fusing spatial and spectral information. By utilizing tensor ring decomposition, HSIs and MSIs undergo feature decomposition, and the objective function is formulated to merge reconstructed results with the original images. Through a multi-stage iterative optimization procedure, the algorithm progressively enhances latent feature representations, leading to super-resolution hyperspectral image reconstruction. The algorithm’s significant achievements are demonstrated through experiments, producing sharper, more detailed high-resolution hyperspectral images (HRIs) with an improved reconstruction quality and retained spectral information. By combining the advantages of graph regularization and tensor ring decomposition, the proposed algorithm showcases substantial potential and feasibility within the domain of hyperspectral image super-resolution.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition

Sun,

Bao,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…These methods extract spatial and spectral components from MS images that are difficult in practice and perform badly in terms of maintaining spectral information. The MRA-based methods [11], [12] assume that the missing spatial information in LR MS images can be extracted from PAN images. As a result, MRA extracts spatial information from PAN images and injects it into interpolated LR MS images.…”

Section: B Traditional Pansharpening Methodsmentioning

confidence: 99%

“…There are four types of pansharpening methods, including component substitution (CS)-based methods [6], [7], [8], [9], [10], multiresolution analysis (MRA)-based methods [11], [12], [13], [14], [15], model-based methods [16], [17], [18], [19], [20], [21], and deep neural networks (DNNs)-based methods [22], [23], [24], [25], [26], [27], [28], [29], [30]. The DNN-based pansharpening methods have been a research focus in recent years.…”

Section: Indexmentioning

confidence: 99%