Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks

Benzenati, Tayeb; Kessentini, Yousri; Kallel, A.

doi:10.1016/j.eswa.2021.115996

Cited by 15 publications

(8 citation statements)

References 40 publications

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“…The compared traditional methods are GSA [9], BDSD [11], SFIM [12], and MTF-GLP [52]. The GAN-based methods include RED-cGAN [29], PsGAN [30], PGMAN [35], and DIGAN [31]. We carry out comparative experiments on the GF-2 and QuickBird data with reduced and full resolutions.…”

Section: Reduced Resolution Experimentsmentioning

confidence: 99%

“…Liu et al [30] developed a deeply CNN-based pansharpening GAN, i.e., PsGAN, consisting of a dual-stream generator and a discriminator, which distinguishes the generated MS image from the reference image. Benzenati et al [31] introduced a detail injection GAN (DI-GAN) constructed by a dual-stream generator and a relativistic average discriminator. RED-cGAN, PsGAN, and DIGAN are supervised approaches trained on degraded resolution data, nevertheless, the products are not satisfactory for applying to full resolution data.…”

Section: Introductionmentioning

confidence: 99%

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Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

Feng

et al. 2023

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

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Panchromatic sharpening (pansharpening) is an important technology for improving the spatial resolution of multispectral (MS) images. The majority of the models are implemented at the reduced resolution, leading to unfavorable results at the full resolution. Moreover, the complicated relationship between MS and panchromatic (PAN) images is often ignored in detail injection. For the mentioned problems, unsupervised generative adversarial networks with recursive mixed-scale feature fusion for pansharpening (RMFF-UPGAN) are modeled to boost the spatial resolution and preserve the spectral information. RMFF-UPGAN comprises a generator and two U-shaped discriminators. A dual-stream trapezoidal branch is designed in the generator to obtain multiscale information. Further, a recursive mixed-scale feature fusion subnetwork is designed. Perform a prior fusion on the extracted MS and PAN features of the same scale. A mixed-scale fusion is conducted on the prior fusion results of the fine-scale and coarse-scale. The fusion is executed sequentially in the above manner building a recursive mixed-scale fusion structure and finally generating key information. A compensation information mechanism is also designed for the reconstruction of key information to compensate for information. A nonlinear rectification block for the reconstructed information is developed to overcome the distortion induced by neglecting the complicated relationship between MS and PAN images. Two U-shaped discriminators are designed and a new composite loss function is defined. The presented model is validated using two satellite data and the outcomes reveal better than the prevalent approaches regarding both visual assessment and objective indicators.

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Section: Reduced Resolution Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

Feng

et al. 2023

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

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“…TFNet [26] Convolutional neural network 0.8585 GTP-PNet [14] Convolutional neural network 0.8768 LDP-Net [16] Convolutional neural network 0.9182 MDSSC-GAN [27] Generative adversarial network 0.9289 PSGAN [28] Generative adversarial network 0.9316 DI-GAN [29] Generative adversarial network 0.9379 discriminator can circumvent the restriction of input scales and provide significant guidance for the generator. • Extensive comparative and ablation experiments are conducted to verify the effectiveness of the proposed method and each module.…”

Section: Algorithm Category Qnrmentioning

confidence: 99%

“…In recent years, deep learning (DL)-based methods have emerged and gained significant attention in the field of pansharpening. The most representative ones are the convolutional neural network (CNN)-based methods [13,15,16,26,31] and the generative adversarial network (GAN)-based methods [27][28][29]32]. These DL-based methods have achieved remarkable performance improvements compared to traditional pansharpening techniques.…”

Section: Related Work Summarymentioning

confidence: 99%

“…MDSSC‐GAN (Multi Discriminators Spatial Spectral Constraints‐GAN) [27] introduces spatial and spectral constraints for better information preservation from the PAN image and the MS image. DI‐GAN (Detail Injection GAN) [29] applies an original loss function and a relativistic discriminator to make the pansharpening result more realistic.…”

Section: Literature Reviewmentioning

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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DUCD: Deep Unfolding Convolutional-Dictionary network for pansharpening remote sensing image

Li,

Yuan,

2024

Expert Systems with Applications

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Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks

Cited by 15 publications

References 40 publications

Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

Triple‐loss driven generative adversarial network for pansharpening

DUCD: Deep Unfolding Convolutional-Dictionary network for pansharpening remote sensing image

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