High-resolution Multispectral Research Articles

Bayesian fusion of multispectral and panchromatic images using a multi-mode and multiorder gradient tensor-based l 1 / 2 sparse model

ABSTRACT In this letter, based on the tensor representation modelling, we propose a multi-mode and multi-order gradient tensor-based non-convex model (M2GTNM) for Bayesian fusion of multispectral (MS) and panchromatic (Pan) images, which aims at producing the high-resolution MS (HRMS) images. Specifically, by modelling the MS image as the order-3 tensor, we mainly develop the multi-mode and multi-order gradient tensor sparse priors of MS image for fusion. For the spectral preservation of low-resolution MS (LRMS) image, the spectral fidelity constraint between HRMS and LRMS images is imposed. For the spatial-mode prior modelling, the multi-order spatial gradient tensor-based non-convex l 1 / 2 sparse prior between HRMS and Pan images is particularly imposed. Moreover, for the spectral-mode prior modelling, the spectral gradient tensor-based non-convex l 1 / 2 sparse prior between HRMS and upsampled LRMS images is further imposed. Then, we apply the alternating direction method of multipliers to optimize the proposed model. Finally, the reduced-scale and full-scale fusion experiments both validate the effectiveness of M2GTNM method.

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

The goal of pansharpening methods is to complement the spectral and spatial information contained in Multi-spectral (MS) and panchromatic (PAN) images to obtain the desired High-resolution multispectral (HRMS) image. The existing majority of pansharpening methods either extract feature information separately from the MS image and PAN image, or extract feature information after concatenating the MS image and PAN image. However, the entire extraction process lacks the utilization of complementary information and tends to generate redundant information, thereby leading to the loss of certain important information during the extraction process, which in turn affects the overall performance. In order to better utilize the complementary information between the MS image and PAN image and enhance the interpretability of the network, we propose the Deep Unfolding Convolutional-Dictionary Network (DUCD) for pansharpening in this paper. This network fully integrates complementary information between the MS image and PAN image to generate the final fused image. The entire network structure consists of two parts: The encoder and the decoder. In the encoder part of the network, we clarify the common and unique feature information between MS and PAN images by constructing an observation model. Simultaneously, we use the approximate gradient algorithm to continuously optimize the model and iteratively unfold it into a deep network structure. In the decoder part of the network, we concatenate the obtained common and specific information from MS and PAN images and pass them through convolutional and activation layers. Subsequently, they are input into the introduced Frequency Domain-based Transformer (FDT) module and an information-lossless inversible neural network(INN). This provides a more efficient method for establishing long-range dependency relationships between feature extraction and feature fusion. To demonstrate the effectiveness of our proposed method, we conduct extensive experiments on three benchmark datasets QB, GF2 and WV3. Experimental results show that our method outperforms the current SOTA Pansharpening methods in terms of performance.

Transformer-based dual path cross fusion for pansharpening remote sensing images

ABSTRACT The purpose of pan-sharpening is to generate high-resolution multispectral (HRMS) images by combining multispectral images (MS) and panchromatic images (PAN), which have become an important part of remote sensing image processing. Therefore, how to better extract complete feature information from MS images and PAN images has become the focus of our attention. In this paper, we propose a new pansharpening method, called Transformer-based Dual-path cross fusion network (TDF) for Pan-sharpening remote sensing images, which aims to extract the spatial details of PAN images while maintaining the spectral fidelity of MS images. The whole network structure can be divided into two parts: in the encoder part, we adopt the Swin-Transformer module for the downsampling operation, which expands the sensory field of the network to the feature map, and then extracts the global information. However, since the Swin-Transformer module is not good at extracting pixel-level details, we introduce the Base Feature Extraction (BFE) and the Invertible Neural Network Block (INNB) modules for the interaction between the local and the global feature information. we also introduce the Edge-Enhancement Block (EEB) to further enhance the feature extraction at multi-scales during the image fusion process. In the decoder section, we once again employ the Swin Transformer module for downsampling. After the convolution operation and activation function, we utilize the Sub-Pixel Convolutional Neural Network for upsampling to generate the ultimate high-resolution multispectral images. Simulation experiments and real experiments are conducted on QuickBird (QB) and WorldView2 (WV2) datasets, which demonstrated our method are superior to the current methods.

Pan-sharpening via intrinsic decomposition knowledge distillation

Existing deep-learning-based pan-sharpening strategies mainly involve the fusion of panchromatic and multispectral (MS) information at both the pixel and feature levels. In this paper, we hypothesize that the MS image can be expressed as the multiplication of reflectance and illumination components, and that the reflection components of low-resolution (LR) MS and high-resolution (HR) MS images are invariant. Here, the spectral reflection component can effectively describe the spectral response of an object, while the illumination component can effectively describe its texture. Based on this hypothesis, we propose a novel and concise pan-sharpening framework called intrinsic decomposition knowledge distillation. Specifically, the teacher network decomposes the HR MS image into reflectance and illumination components, which are then combined in the student network with the reflectance component and the enhanced illumination component from LR MS to reconstruct the pan-sharpened image. To approximate the component distributions from the teacher network, we introduce a novel three-stage knowledge distillation strategy that can transfer knowledge about the relationships between components and constrain the student network. Our quantitative and qualitative comparisons demonstrate the reasonableness of our hypothesis and the effectiveness of our proposed method in significantly improving perception quality.

Rethinking Pan-Sharpening in Closed-Loop Regularization.

It is generally known that pan-sharpening is fundamentally a PAN-guided multispectral (MS) image super-resolution problem that involves learning the nonlinear mapping from low-resolution (LR) to high-resolution (HR) MS images. Since an infinite number of HR-MS images can be downsampled to produce the same corresponding LR-MS image, learning the mapping from LR-MS to HR-MS image is typically ill-posed and the space of the possible pan-sharpening functions can be extremely large, making it difficult to estimate the optimal mapping solution. To address the above issue, we propose a closed-loop scheme that learns the two opposite mapping including the pan-sharpening and its corresponding degradation process simultaneously to regularize the solution space in a single pipeline. More specifically, an invertible neural network (INN) is introduced to perform a bidirectional closed-loop: the forward operation for LR-MS pan-sharpening and the backward operation for learning the corresponding HR-MS image degradation process. In addition, given the vital importance of high-frequency textures for the Pan-sharpened MS images, we further strengthen the INN by designing a specified multiscale high-frequency texture extraction module. Extensive experimental results demonstrate that the proposed algorithm performs favorably against state-of-the-art methods qualitatively and quantitatively with fewer parameters. Ablation studies also verify the effectiveness of the closed-loop mechanism in pan-sharpening. The source code is made publicly available at https://github.com/manman1995/pan-sharpening-Team-zhouman/.

A variational driven optimization framework for pansharpening of multispectral images

ABSTRACT Pansharpening is an established remote sensing image fusion technique that yields a high-resolution multispectral (HRMS) image. Despite the fact that the advanced technologies like sparse coding and deep learning have achieved a remarkable improvement in solving the pansharpening problem, a unified model is required to further enhance the fusion quality. The variational optimisation (VO) mechanism has gained interest of most of the researchers in recent years. In this article, pansharpening is designated as a constrained optimisation problem with a data generative term and two regularisers to ameliorate spatial details and spectral information. The gradient information is exploited to impart the spatial details from the panchromatic image to the fused image. The correlation among multispectral image bands inspired to promote the spectral quality of the HRMS image as well as to reduce the distortion. Consequently, the optimisation problem is efficiently solved for the required HRMS image using the operator splitting approach. The extensive experiments performed in accordance with the well-known protocols rationalise that the proposed model outperforms most of the state-of-the-art methods in terms of objective metrics and visual outcomes.

A Triple-Double Convolutional Neural Network for Panchromatic Sharpening.

Pansharpening refers to the fusion of a panchromatic (PAN) image with a high spatial resolution and a multispectral (MS) image with a low spatial resolution, aiming to obtain a high spatial resolution MS (HRMS) image. In this article, we propose a novel deep neural network architecture with level-domain-based loss function for pansharpening by taking into account the following double-type structures, i.e., double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the PAN image can be fully exploited and utilized to progressively inject into the low spatial resolution MS (LRMS) image, thus yielding the high spatial resolution output. The specific network design is motivated by the physical formula of the traditional multi-resolution analysis (MRA) methods. Hence, an effective MRA fusion module is also integrated into the TDNet. Besides, we adopt a few ResNet blocks and some multi-scale convolution kernels to deepen and widen the network to effectively enhance the feature extraction and the robustness of the proposed TDNet. Extensive experiments on reduced- and full-resolution datasets acquired by WorldView-3, QuickBird, and GaoFen-2 sensors demonstrate the superiority of the proposed TDNet compared with some recent state-of-the-art pansharpening approaches. An ablation study has also corroborated the effectiveness of the proposed approach. The code is available at https://github.com/liangjiandeng/TDNet.

Triple‐loss driven generative adversarial network for pansharpening

AbstractPansharpening 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.

Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion for remote sensing image

ABSTRACT Remote sensing image (RSI) with concurrently high spatial, temporal, and spectral resolutions cannot be produced by a single sensor. Multisource RSI fusion is a convenient technique to realize high spatial resolution multispectral (MS) images (spatial spectral fusion, i.e. SSF) and high temporal and spatial resolution MS images (spatiotemporal fusion, i.e. STF). Currently, deep learning-based fusion models can only implement SSF or STF, lacking models that perform both SSF and STF. Multiresolution generative adversarial networks with bidirectional adaptive-stage progressive guided fusion (BAPGF) for RSI are proposed to implement both SSF and STF, namely BPF-MGAN. A bidirectional adaptive-stage feature extraction architecture in fine-scale-to-coarse-scale and coarse-scale-to-fine-scale modes is introduced. The designed BAPGF introduces a previous fusion result-oriented cross-stage-level dual-residual attention fusion strategy to enhance critical information and suppress superfluous information. Adaptive resolution U-shaped discriminators are implemented to feed multiresolution context into the generator. A generalized multitask loss function unlimited by no-reference images is developed to strengthen the model via constraints on the multiscale feature, structural, and content similarities. The BPF-MGAN model is validated on SSF datasets and STF datasets. Compared with the state-of-the-art SSF and STF models, results demonstrate the superior performance of the proposed BPF-MGAN model in both subjective and objective evaluations.

FrMLNet: Framelet-Based Multilevel Network for Pansharpening.

Most modern satellites can provide two types of images: 1) panchromatic (PAN) image and 2) multispectral (MS) image. The former has high spatial resolution and low spectral resolution, while the latter has high spectral resolution and low spatial resolution. To obtain images with both high spectral and spatial resolution, pansharpening has emerged to fuse the spatial information of the PAN image and the spectral information of the MS image. However, most pansharpening methods fail to preserve spatial and spectral information simultaneously. In this article, we propose a framelet-based convolutional neural network (CNN) for pansharpening which makes it possible to pursue both high spectral and high spatial resolution. Our network consists of three subnetworks: 1) feature embedding net; 2) feature fusion net; and 3) framelet prediction net. Different from conventional CNN methods directly inferring high-resolution MS images, our approach learns to predict their framelet coefficients from available PAN and MS images. The introduction of multilevel feature aggregation and hybrid residual connection makes full use of spatial information of PAN image and spectral information of MS image. Quantitative and qualitative experiments at reduced- and full-resolution demonstrate that the proposed method achieves more appealing results than other state-of-the-art pansharpening methods. The source code and trained models are available at https://github.com/TingMAC/FrMLNet.

A Spectral Enhancement Method Based on Remote-Sensing Images for High-Speed Railways

This paper proposes a pansharpening model in order to obtain remote-sensing images with high spatial resolution and high spectral resolution. Based on a generic component substitution (CS) fusion framework, the model utilizes the difference between the high-frequency component of the panchromatic (PAN) image and the high-frequency component of the luminance (L) image to express the missing spatial detail information of the ideal high-resolution multispectral (HRMS) image. A rolling guidance filter (RGF) is used in this framework to achieve the effective extraction of high-frequency information from remote-sensing images while reducing the spectral distortion of subsequent operations. The modulation transfer function (MTF) values of the sensor are also applied to the selection of adaptive weighting coefficients to further improve the spectral fidelity of the fused images. At the same time, the choice of suitable interpolation and gain coefficients improves the generalizability of the model while reducing spectral and spatial distortions. Finally, the use of a guided filter (GF) also greatly improves the quality of the fused image. The experimental results show that the model can effectively improve the spatial resolution for foreign objects at the perimeter of high-speed railways, while also ensuring the color fidelity of foreign objects such as colored steel tiles.

A deep unrolling pansharpening method based on spectral consistency and double spatial priors

ABSTRACT In recent years, deep learning (DL) pansharpening methods have become the most popular solution for acquiring high-resolution multispectral (HRMS) images. However, most DL-based methods design the task as black-box network architecture, ignoring the physical meaning of individual network modules. Such a network structure is difficult to extract sufficient spatial details or lacks interpretability. To address this problem, we propose a neural network pansharpening method with interpretable deep spatial detail injection based on the assumption of spectral consistency and double spatial detail priors in HRMS images. First, we assume that the HRMS image consists of a spectral and spatial component together. Spectral and partial spatial details are obtained by upsampling the multispectral (MS) images. Next, a variational model is constructed for image fusion from the LRMS image and the panchromatic (PAN) image to the HRMS image.The alternating direction multiplier method (ADMM) algorithm is then used to optimally solve the variational model to obtain the spatial detail that needs to be injected into the MS image. Finally, the model optimization solving process is expanded into a corresponding neural network structure using the idea of deep unrolling. Each network module corresponds to the solving step of the iterative algorithm. Our proposed pansharpening method is therefore interpretable in terms of spatial detail injection. In our experiments, we find that the proposed method achieves an optimal balance between spectral and spatial quality, and has a strong generalization capability over different datasets, demonstrating the superiority of the proposed pansharpening method.

Intensity mixture and band-adaptive detail fusion for pansharpening

Pansharpening aims to sharpen a low-resolution multispectral (MS) image through a high-resolution single-channel panchromatic (PAN) image to obtain a high-resolution multi-spectral (HRMS) image. However, low correlation between the PAN and MS images, as well as the inaccurate detail injection for each band of MS image are the key problems causing spectral and spatial distortions in pansharpening. To address these issues, a new pansharpening method based on the intensity mixture and band-adaptive detail fusion is proposed. To obtain a mixed-intensity image (T) that has a high correlation with the MS image and maintain the gradient information of the PAN image, the intensity mixture model is constructed by establishing the intensity and gradient constraints between T and the source images. As it is hard to obtain a proper degradation filter in the model, a filter estimation algorithm is designed by the distribution alignment. To inject the details that match the point spread function of the sensor, a band-adaptive detail fusion algorithm is presented to fuse the details extracted from T with those from the MS image for each band. Furthermore, as there are far fewer details in the MS image than in T, a detail enhancement algorithm is proposed to enhance the details proportionally. The final HRMS image is obtained by injecting the fused details into the upsampled MS image. Extensive experiments show that the proposed method can efficiently achieve the best results in fusion quality compared to state-of-the-art methods. The code is availabe at https://github.com/yotick/IMBD.

Pan-sharpening of remote sensing images based on gradient projection and cross fusion

ABSTRACT Pan-sharpening is the fusion of panchromatic (PAN) image and multispectral (MS) image through certain rules to generate high-resolution multispectral (HRMS) image with vivid spatial details and uniform spectral distribution. It has become an important technology in remote sensing image processing. Recently, convolutional neural networks based on deep learning have achieved remarkable results in the field of pan-sharpening. Guided by this method, this paper proposes a pan-sharpening network PSAM-NET for remote sensing images based on depth expansion combined with Cross-Attention Fusion. It consists of gradient projection and the main image fusion module. The gradient projection mainly generates the fusion module by stacking low-resolution multispectral images and panchromatic images alternately, and two deep prior regularized optimization problem formulas are respectively solved by the gradient projection algorithm. The other principal image fusion module realizes double branch fusion, which is mainly composed of cross attention fusion and channel attention fusion, to produce an excellent fusion effect. The simulation and real data experiments were performed on the standard datasets WV2, GF-2, and QB. The qualitative analysis and quantitative comparison with the classical pan-sharpening method proved that the spatial information of the image obtained by this method is complete. The spectral distribution is uniform, and the evaluation index also has some advantages.

Pansharpening With Spatial Hessian Non-Convex Sparse and Spectral Gradient Low Rank Priors.

To get the high resolution multi-spectral (HRMS) images by the fusion of low resolution multi-spectral (LRMS) and panchromatic (PAN) images, an effectively pansharpening model with spatial Hessian non-convex sparse and spectral gradient low rank priors (PSHNSSGLR) is proposed in this paper. In particularly, from the statistical aspect of view, the spatial Hessian hyper-Laplacian non-convex sparse prior is developed to model the spatial Hessian consistency between HRMS and PAN. More importantly, it is recently the first work for pansharpening modeling with the spatial Hessian hyper-Laplacian non-convex sparse prior. Meanwhile, the spectral gradient low rank prior on HRMS is further developed for spectral feature preservation. Then, the alternating direction method of multipliers (ADMM) approach is applied for optimizing the proposed PSHNSSGLR model. Afterwards, many fusion experiments demonstrate the capability and superiority of PSHNSSGLR.

CADUI: Cross-Attention-Based Depth Unfolding Iteration Network for Pansharpening Remote Sensing Images

Pansharpening is an important technology for remote sensing imaging systems to obtain high-resolution multispectral (HRMS) images. It mainly obtains high-resolution multi-spectral (HRMS) images with uniform spectral distribution and rich spatial details by fusing low-resolution multi-spectral (LRMS) images and high-spatial-resolution panchromatic (PAN) images. Therefore, how to extract features completely and reconstruct images with high quality is critical to obtain ideal fusion images. In this paper, we propose a new pansharpening method, called the Cross Attention-based Depth Unfolding Iteration Network for Pan-sharpening remote sensing images (CADUI), which achieves the desired fusion effect by iteratively optimizing the deep prior regularization and combining it with a cross-attention mechanism. The network consists of two parts: optimized iterations of deep prior regularization (DEIN-Block) and cross-attention mechanism (CAFM-Block). Among them, DEIN-Block introduces the depth prior as an implicit regularization and improves the adaptability and representation ability of the relevant data of the reconstructed image through iteration. CAFM-Block realizes dual-branch fusion through cross-attention fusion and channel-attention fusion to achieve better fusion results. Simulation experiments and real experiments are carried out on the standard datasets QuikBird (QB) and WorldView-2 (WV2). Through quantitative comparison and qualitative analysis, it is proved that the method is superior to the existing methods.

AWFLN: An Adaptive Weighted Feature Learning Network for Pansharpening

Deep learning (DL)-based pansharpening methods have shown great advantages in extracting spectral-spatial features from multispectral (MS) and panchromatic (PAN) images compared with traditional methods. However, most DL-based methods ignore the local inner connection between the source images and high-resolution MS (HRMS) image, which cannot fully extract spectral-spatial information, and attempt to improve the quality of fusion by increasing the complexity of the network. To solve these problems, a lightweight network based on adaptive weighted feature learning (AWFLN) is proposed for pansharpening. Specifically, a novel detail extraction model is first built by exploring the local relationship between HRMS and source images, thereby improving the accuracy of details and the interpretability of the network. Guided by this model, we then design a residual multiple receptive-field structure to fully extract spectral-spatial features of source images. In this structure, an adaptive feature learning block based on spectral-spatial interleaving attention is proposed to adaptively learn the weights of features and improve the accuracy of the extracted details. Finally, the pansharpened result is obtained by a detail injection model in AWFLN. Numerous experiments are carried out to validate the effectiveness of the proposed method. Compared to traditional and state-of-the-art methods, AWFLN performs the best both subjectively and objectively, with high efficiency. The code is available at https://github.com/yotick/AWFLN.

Hyperspectral Image Superresolution via Subspace-Based Deep Prior Regularization

Hyperspectral imaging is able to provide a finer delivery of various material properties than conventional imaging systems. Yet in reality, an optical system can only generate data with high spatial resolution but low spectral one, or vice versa, at video rates. As a result, an issue that fuses low-resolution hyperspectral (LRHS) and high-resolution multispectral (HRMS) images has gained great attention. However, most fusion approaches depend purely on hand-crafted regularizers or data-driven priors, leading to the issues of tricky parameter selection or poor interpretability. In this work, a subspace-based deep prior regularization (SDPR) is proposed to tackle these problems, which takes both hand-crafted regularizer and data-driven prior into account. Specifically, we leverage the spectral correlation of the images and transfer them from the original space to the subspace domain, within which a modified U-net-based deep prior learning network (SDPL-net) is designed for the fusion issue. Moreover, instead of taking the output of SDPL-net directly as the result, we further feed the output back to the model-based optimization. Under such prior regularization, the recovered high-resolution hyperspectral (HRHS) image holds a high consistency to its inherent structure and hence tends to present enhanced reliability and accuracy. Experimental results on simulated and real data reveal that the proposed method excels other state-of-the-art (SOTA) methods in both quantitative and qualitative metrics.

Unsupervised Pansharpening Method Using Residual Network With Spatial Texture Attention

Recently, deep learning has become one of the most popular tools for pansharpening, many relevant methods have been investigated and reflected great performance. However, a non-negligible problem is the absence of ground-truth (GT). A common solution is using degraded images as training input and the original images are employed as GT. The learned mapping between low resolution (LR) and high resolution (HR) is simulated, is not real, which may cause spectral distortion or insufficient spatial texture enhancement of fused images. In order to address the drawback, a novel unsupervised attention pansharpening net (UAP-Net) is proposed. The proposed UAP-Net mainly contains two major components: 1) the deep residual network (DRN) and 2) spatial texture attention block (STAB). The DRN aims to extract spectral features and spatial details features from low-resolution multi-spectral (LRMS) and panchromatic (PAN), and to fuse those features to make them more representative. The designed STAB adopts the high-frequency component of corresponding input PAN as the weight to enhance the spatial details of the residual block output features. Moreover, a new loss function including two spatial losses and two spectral losses are established. The losses are calculated in the spatial domain and the frequency domain, respectively. Experiments on Gaofen-2 and Worldview-2 remote sensing data demonstrate that the proposed UAP-Net could fuse PAN and LRMS images effectively without the help of high-resolution multi-spectral (HRMS). The proposed framework is fully general and can be used for many multisource remote sensing image fusion, and achieves optimal performance in terms of both the subjective visual effect and the quantitative evaluation.

Mun-GAN: A Multiscale Unsupervised Network for Remote Sensing Image Pansharpening

In remote sensing image fusion, pansharpening is a type of remote sensing image fusion method that aims to fuse panchromatic (PAN) images and multispectral (MS) images to produce high-resolution multispectral (HRMS) images. Deep learning based pansharpening technology offers a series of advanced unsupervised algorithms. However, there are several challenges: (1) The existing unsupervised pansharpening methods only consider the fusion of single-scale features; (2) for the fusion of MS and PAN image feature branches, the existing pansharpening methods are implemented directly by concatenation and summation, without paying attention to critical features or suppressing redundant features; (3) the semantic gap in the long skip connections of the network architecture will create unexpected results. In this paper, we design a multiscale unsupervised architecture based on generative adversarial networks (GANs) for remote sensing image pansharpening (Mun-GAN), which consists of a generator and two discriminators. The generator includes a multi-scale feature extractor (MFE), a self-adaptation weighted fusion (SWF) module, and a nest feature aggregation (NFA) module. First, the MFE is utilized to extract multiscale feature information from the input images and to then pass this information to the SWF module for adaptive weight fusion. Then, multiscale features are reconstructed by the NFA module to obtain HRMS images. The two discriminators are spectral and spatial discriminators used against the generator. Moreover, we design a hybrid loss function to aggregate the multiscale spectral and spatial feature information. Compared with other state-of-the-art methods using QuickBird, GaoFen-2 and WorldView-3 images, which demonstrate that the Mun-GAN yields better fusion results.