A novel iterative PCA–based pansharpening method

Gabsi, Mohamed; Moussaoui, Abdelkrim; Boukharouba, Abdelhak

doi:10.1080/2150704x.2018.1547443

Cited by 21 publications

(6 citation statements)

References 11 publications

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“…Traditional pansharpening methods can be categorized into three primary approaches: component substitution (CS)-based methods [3][4][5], multi-resolution analysis (MRA)-based methods [6][7][8], and variational optimization (VO)-based methods [9][10][11]. CS-based methods involve transforming the multispectral (MS) image into a different space and substituting its spatial components with those of the panchromatic (PAN) image.…”

Section: Related Work Summarymentioning

confidence: 99%

“…In preliminary studies, various traditional methods were proposed to solve pansharpening problems, and most of these methods can be roughly classified into three categories: component substitute (CS)-based methods [3][4][5], multi-resolution analysis (MRA)-based methods [6][7][8], and variational optimization (VO)-based methods [9][10][11]. CS-based methods normally perform well in terms of preserving spatial information, but they may induce spectral distortion.…”

Section: Introductionmentioning

confidence: 99%

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Triple‐loss driven generative adversarial network for pansharpening

Huang,

Li,

Zhang

2023

IET Image Processing

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Pansharpening aims at fusing a panchromatic (PAN) image and a low‐resolution multispectral (LRMS) image into a high‐resolution multispectral (HRMS) image. In recent years, GAN‐based pansharpening methods have achieved excellent results, but they suffer from inadequate feature preservation and unstable training. To address these issues, a novel GAN‐based model named TriLossGAN is proposed. This method constructs three loss components with the help of the generator and the dual‐discriminator, which are calculated in both the original spatial domain and the transform domain to better preserve high‐frequency and low‐frequency information in the fused image. Additionally, a new training strategy is designed to stabilize the training process. In extensive experiments, the proposed method achieved satisfactory results on three datasets with QNR values of 0.9584 on GaoFen‐2, 0.9601 on QuickBird, and 0.9138 on WorldView‐3. Qualitative and quantitative comparisons demonstrate that TriLossGAN outperforms other state‐of‐the‐art methods.

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Section: Related Work Summarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Triple‐loss driven generative adversarial network for pansharpening

Huang,

Li,

Zhang

2023

IET Image Processing

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“…This dependence is presented in the following equation, where the HPF is the high-pass filtration of the PAN image (see Fig. 3) [75]:…”

Section: B Pansharpeningmentioning

confidence: 99%

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

Karwowska

Wierzbicki

2022

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

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“…Traditional methods can be categorized into Component Substitution (CS), Multi-Resolution Analysis (MRA), and model-based approaches. Gram-Schmidt transformation (GS) 1 , Principal Component Analysis (PCA) 2 , and the Band-dependent Spatial Detail (BDSD) 3 , among other methods, are examples of common CS methods. These methods aim to substitute the space components of multispectral images with panchromatic images while retaining as much of the original spectral information as possible.…”

Section: Introductionmentioning

confidence: 99%

GF-CSTNet: A Method for Pan-Sharpening Remote Sensing Images by Integrating CSPNet and Transformer

Chen,

Liu,

Fang

2024

Preprint

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To tackle challenges like limited receptive fields and inadequate feature extraction encountered in conventional ConvolutionalNeural Networks (CNNs), this paper introduces a remote sensing image sharpening approach named GF-CSTNet. Themethod draws inspiration from the FusionNet architecture and combines CSPNet with Transformer for improved performance.Firstly, this method utilizes guided filtering to enhance the acquired remote sensing image data. The CSPNet and Transformerstructures are then combined to further enhance fusion performance by leveraging their respective advantages. Subsequently, aRep-Conv2Former method is designed to streamline attention and extract diverse receptive field features through a multi-scaleconvolution modulator block. Simultaneously, a reparameterization module is constructed to integrate the multiple branchesgenerated during training into a unified branch during inference, thereby optimizing the model’s inference speed. Furthermore,a residual learning module incorporating attention has been devised to augment the modeling and feature extraction capabilitiesof images. Finally, to tackle the overfitting issue that emerges during training, weight decay has been incorporated into the lossfunction. Experimental results obtained from the GaoFen-2 and WorldView-3 datasets demonstrate the effectiveness of theproposed GF-CSTNet approach. It effectively extracts detailed information from images while avoiding the problem of spectraldistortion.

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A novel iterative PCA–based pansharpening method

Cited by 21 publications

References 11 publications

Triple‐loss driven generative adversarial network for pansharpening

Triple‐loss driven generative adversarial network for pansharpening

Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review

GF-CSTNet: A Method for Pan-Sharpening Remote Sensing Images by Integrating CSPNet and Transformer

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