Fusion Problem Research Articles

A patent text-based product conceptual design decision-making approach considering the fusion of incomplete evaluation semantic and scheme beliefs

Conceptual design evaluation is a significant challenge in product development to select suitable conceptual schemes (CS). Relying on decision makers' (DMs) personal experience for design information may result in incomplete and ambiguous evaluation semantics, creating an incomplete decision matrix. Previous approaches use attributes based on candidate schemes to complete the missing information, but neglect the mining of associated design information from external resources such as patents, reducing the beliefs objectivity after missing semantic completion. Besides, non-compensatory nature between DMs’ interactivity and criteria have not been well considered. To fill these issues, a patent text-based CS decision approach considering the fusion of incomplete semantics and scheme beliefs is proposed. First, using the beneficial effects text from patents as historical design data, constructing general evaluation criteria using the Latent Dirichlet Allocation topic model, and introducing the Apriori model to extract strong association rules between criteria to complete the incomplete semantics. Second, a fuzzy measure model for DMs is constructed based on intuitionistic fuzzy cross-entropy and Shapley value method, supporting the allocation of DMs' interactive weights. Then, the fuzzy Dempster-Shafer evidence theory is used to transform the multi-criteria decision model into a belief fusion problem of CS that avoids aggregating the evaluation semantics of non-compensatory criteria and outputting the optimal CS with interval credibility. A practical case study of an in-pipe inspection robot will be employed to validate the proposed approach, sensitivity analysis and comparison results confirmed that the complement data have the reliability to avoid continuous investment in the evaluation process.

Interval-based sparse ensemble multi-class classification algorithm for terahertz data

Terahertz time-domain spectroscopy (THz-TDS) has been widely used for food and drug identification. The classification information of a THz spectrum usually does not exist in the whole spectral band but exists only in one or several small intervals. Therefore, feature selection is indispensable in THz-based substance identification. However, most THz-based identification methods empirically intercept the low-frequency band of the THz absorption coefficients for analysis. In order to adaptively find out important intervals of the THz spectra, an interval-based sparse ensemble multi-class classifier (ISEMCC) for THz spectral data classification is proposed. In ISEMCC, the THz spectra are first divided into several small intervals through window sliding. Then the data of training samples in each interval are extracted to train some base classifiers. Finally, a final robust classifier is obtained through a nonnegative sparse combination of these trained base classifiers. With l1 -norm, two objective functions that based on Mean Square Error (MSE) and Cross Entropy (CE) are established. For these two objective functions, two iterative algorithms based on the Alternating Direction Method of Multipliers (ADMM) and Gradient Descent (GD) are built respectively. ISEMCC transforms the problem of interval feature selection and decision-level fusion into a nonnegative sparse optimization problem. The sparse constraint ensures only a few important spectral segments are selected. In order to verify the performance of the proposed algorithm, comparative experiments on identifying the origin of Bupleurum and the harvesting year of Tangerine peel are carried out. The base classifiers used by ISEMCC are Support Vector Machine (SVM) and Decision Tree (DT). The experimental results demonstrate that the proposed algorithm outperforms six typical classifiers, including Random Forest (RF), AdaBoost, RUSBoost, ExtraTree, and the two base classifiers, in terms of classification accuracy.

Multi-scale spatial and spectral feature fusion for soil carbon content prediction based on hyperspectral images

Soil carbon content prediction based on hyperspectral images can achieve large-scale spatial measurement, which has the advantages of wide coverage and fast information collection, is more suitable for field data collection. However, the research on soil carbon content prediction based on hyperspectral images mainly focuses on feature extraction of spectral information, ignoring the spatial information, and cannot well reveal the intrinsic structural characteristics of data. Aiming at the lack of spatial features consideration in hyperspectral images, soil carbon content prediction methods based on multi-scale feature fusion are proposed by hyperspectral image. At the same time of extracting spectral features from hyperspectral images, the spatial information is used for the first time and a multi-scale spectral and spatial feature network (SpeSpaMN) is designed. In the SpeSpaMN, the multi-scale spectral feature network (SpeMN) is constructed to extract spectral features, the multi-scale spatial feature network (SpaMN) is constructed to extract spatial features. The two networks are fused by using the complementary relationship between different scale features to achieve soil carbon content prediction based on multi-scale feature fusion. The results showed that SpeSpaMN had the best results compared to other methods, followed by the method of SpeMN. The RPD of Inland, Aoshan Bay and Jiaozhou Bay samples based on SpeSpaMN were increased by 47.36%, 37.96% and 4.30% respectively. This paper can effectively solve the problem of the deep fusion of spatial and spectral features in the soil carbon content prediction by hyperspectral image, so as to improve the accuracy and stability of soil carbon content prediction.

A YOLOX Object Detection Algorithm Based on Bidirectional Cross-scale Path Aggregation

To solve the problem of insufficient feature fusion between the deep and shallow feature layers of the original YOLOX algorithm, which resulting in a loss of object semantic information, this paper proposes a YOLOX object detection algorithm based on attention and bidirectional cross-scale path aggregation. First, an efficient channel attention module is embedded in the YOLOX backbone network to reinforce the key features in the object region by distinguishing between the importance of the different channels in the feature layer, thus enhancing the detection accuracy of the network. Second, a bidirectional cross-scale path aggregation network is designed to change the information fusion circulation path while increasing the cross-scale connections. Weighted feature fusion is used to learn the importance of the different path input features for differentiated fusion, thereby improving the feature information fusion capability between the deep and shallow layers. Finally, the SIOU loss function is introduced to improve the detection performance of the network. The experimental results show that on the PASCAL VOC2007 and MS COCO2017 datasets, the algorithm in this paper improves mAP by 2.32% and 1.53% compared with the original YOLOX algorithm, and has comprehensive performance advantages compared with other algorithms. The mAP reaches 99.44% on the self-built iron ore metal foreign matter dataset, with a recognition speed of 56.90 frames/s.

A Spatio-Spectral Fusion Method for Hyperspectral Images Using Residual Hyper-Dense Network.

Spatio-spectral fusion of panchromatic (PAN) and hyperspectral (HS) images is of great importance in improving spatial resolution of images acquired by many commercial HS sensors. DenseNets have recently achieved great success for image super-resolution because they facilitate gradient flow by concatenating all the feature outputs in a feedforward manner. In this article, we propose a residual hyper-dense network (RHDN) that extends the DenseNet to solve the spatio-spectral fusion problem. The overall structure of the proposed RHDN method is a two-branch network, which allows the network to capture the features of HS images within and outside the visible range separately. At each branch of the network, a two-stream strategy of feature extraction is designed to process PAN and HS images individually. A convolutional neural network (CNN) with cascade residual hyper-dense blocks (RHDBs), which allows direct connections between the pairs of layers within the same stream and those across different streams, is proposed to learn more complex combinations between the HS and PAN images. The residual learning is adopted to make the network efficient. Extensive benchmark evaluations well demonstrate that the proposed RHDN fusion method yields significant improvements over many widely accepted state-of-the-art approaches.

The Multisensor Data Fusion Method Based on Improved Fuzzy Evidence Theory in the Coal Mine Environment

An enhanced evidence theory-based multisensor data fusion technique is presented to address the problem of poor data fusion caused by an unknown interference in the fully automated mining face multisensor system of a coal mine. Initially, the set of all measurement values is considered as the identification framework, and the principles of fuzzy mathematics are applied to introduce the membership function. This leads to the proposal of a novel method for calculating mutual support among multiple sensors. Furthermore, the basic belief assignment (BBA) in evidence theory is determined by measuring the confidence distance between sensors. Subsequently, a divergence measure is employed to assess the level of conflict and difference between BBA functions, which serves as an indicator of their credibility. The credibility of BBA functions is further adjusted by calculating their information volume using Shannon entropy. This adjustment aims to increase the weight of BBA functions that exhibit less conflict with other BBA functions. Ultimately, the fusion result is obtained through an evidence combination rule based on a conflict allocation. The numerical experimental results demonstrate that the proposed approach achieves higher accuracy, better robustness, and generality compared to the existing methods.

FedTKD: A Trustworthy Heterogeneous Federated Learning Based on Adaptive Knowledge Distillation.

Federated learning allows multiple parties to train models while jointly protecting user privacy. However, traditional federated learning requires each client to have the same model structure to fuse the global model. In real-world scenarios, each client may need to develop personalized models based on its environment, making it difficult to perform federated learning in a heterogeneous model environment. Some knowledge distillation methods address the problem of heterogeneous model fusion to some extent. However, these methods assume that each client is trustworthy. Some clients may produce malicious or low-quality knowledge, making it difficult to aggregate trustworthy knowledge in a heterogeneous environment. To address these challenges, we propose a trustworthy heterogeneous federated learning framework (FedTKD) to achieve client identification and trustworthy knowledge fusion. Firstly, we propose a malicious client identification method based on client logit features, which can exclude malicious information in fusing global logit. Then, we propose a selectivity knowledge fusion method to achieve high-quality global logit computation. Additionally, we propose an adaptive knowledge distillation method to improve the accuracy of knowledge transfer from the server side to the client side. Finally, we design different attack and data distribution scenarios to validate our method. The experiment shows that our method outperforms the baseline methods, showing stable performance in all attack scenarios and achieving an accuracy improvement of 2% to 3% in different data distributions.

New insights into multi-focus image fusion: A fusion method based on multi-dictionary linear sparse representation and region fusion model

This paper proposes a method based on multi-dictionary linear sparse representation and region fusion model for achieving high-quality multi-focus image fusion (MFIF). First, a multi-dictionary linear sparse representation model (MDLSR) is proposed for focus detection of source images, in which the focus information of the source image is characterized using linear coefficients between the dictionary and the input image, while the dictionary is a set of images including the source images. MDLSR exploits the correlation of feature information among local neighborhoods of the source image, and this feature significantly improves the accuracy of focus detection. Next, we propose an edge-weighting closed-form matting (EWCM) to define the source image focus boundary accurately. EWCM calculates the weights at each position in a content-adaptive manner, suppressing the effects of vignetting artifacts near the edges to preserve more edge details. Then, to avoid the focused/defocused boundary (FDB) region fusion problem caused by the defocus spread effect (DSE), we propose a region fusion model (RFM) for the MFIF, which uses correction and compensation to mitigate this problem. Finally, we also adopt an iterative update approach to improve the quality of the fused images. These designs achieve a win-win situation regarding fusion performance and realization ability. The experimental results on four typical datasets show that our method outperforms 37 state-of-the-art methods in terms of subjective visual effect, objective metric analysis and generalization ability. Our code is available at https://github.com/JWatawang/MDLSR_RFM.

A Principle Design of Registration-Fusion Consistency: Toward Interpretable Deep Unregistered Hyperspectral Image Fusion.

For hyperspectral image (HSI) and multispectral image (MSI) fusion, it is often overlooked that multisource images acquired under different imaging conditions are difficult to be perfectly registered. Although some works attempt to fuse unregistered images, two thorny challenges remain. One is that registration and fusion are usually modeled as two independent tasks, and there is no yet a unified physical model to tightly couple them. Another is that deep learning (DL)-based methods may lack sufficient interpretability and generalization. In response to the above challenges, we propose an unregistered HSI fusion framework energized by a unified model of registration and fusion. First, a novel registration-fusion consistency physical perception model (RFCM) is designed, which uniformly models the image registration and fusion problem to greatly reduce the sensitivity of fusion performance to registration accuracy. Then, an HSI fusion framework (MoE-PNP) is proposed to learn the knowledge reasoning process for solving RFCM. Each basic module of MoE-PNP one-to-one corresponds to the operation in the optimization algorithm of RFCM, which can ensure clear interpretability of the network. Moreover, MoE-PNP captures the general fusion principle for different unregistered images and therefore has good generalization. Extensive experiments demonstrate that MoE-PNP achieves state-of-the-art performance for unregistered HSI and MSI fusion. The code is available at https://github.com/Jiahuiqu/MoE-PNP.

Optimized path planning and scheduling strategies for connected and automated vehicles at single-lane roundabouts.

This paper focuses on the cooperative driving challenges of connected and automated vehicles (CAVs) at single-lane roundabouts. First, a geometric path planning method is proposed for CAVs navigating a single-lane roundabout. Based on this method, a vehicle roundabout model is established. Four potential traffic scenarios for CAVs are established, and the optimal arrival times at conflict points are analyzed. By correlating the optimal arrival times at conflict points with the optimal entry times into the roundabout, the multi-vehicle coordination problem in complex intersections is simplified to a speed control issue during entry. Utilizing the principles of optimal control and Pontryagin minimization, two speed optimization strategies are proposed. Finally, MATLAB is employed for simulation analysis. The results indicate that the control strategy proposed in this paper enables the system to clearly identify potential conflicts between vehicles and implement an optimal control strategy, ensuring that vehicles can navigate the roundabout efficiently in terms of time and fuel without collisions. Additionally, the minimum time interval is established at 0.2 seconds to completely prevent vehicle collisions. In this study, the fusion problem involving two vehicles at a single conflict point is further expanded to encompass multiple vehicles at multiple conflict points. Thus, the efficient scheduling of multiple vehicles in single-lane roundabouts is realized.

Deep Variational Network for Blind Pansharpening.

Deep-learning-based methods play an important role in pansharpening that uses panchromatic images to enhance the spatial resolution of multispectral images while maintaining spectral features. However, most existing methods mainly consider only one fixed degradation in the training process. Therefore, their performance may drop significantly when the degradation of testing data is unknown (blind) and different from the training data, which is common in real-world applications. To address this issue, we proposed a deep variational network for blind pansharpening, named VBPN, which integrates degradation estimation and image fusion into a whole Bayesian framework. First, by taking the noise and blurring parameters of the multispectral image with the noise parameters of the panchromatic image as hidden variables, we parameterize the approximate posterior distribution for the fusion problem using neural networks. Since all parameters in this posterior distribution are explicitly modeled, the degradation parameters of the multispectral image and the panchromatic image are easily estimated. Furthermore, we designed VPBN composed of degradation estimation and image fusion subnetworks, which can optimize the fusion results guided by the variational inference according to the testing data. As a result, the blind pansharpening performance can be improved. In general, VPBN has good interpretability and generalization ability by combining the advantages of model-based and deep-learning-based approaches. Experiments on simulated and real datasets prove that VPBN can achieve state-of-the-art fusion results.

Integrating crystal structure and numerical data for predictive models of lithium-ion battery materials: A modified crystal graph convolutional neural networks approach

Machine learning (ML) are currently making significant impact and bringing tremendous opportunities in material science, and has proven to be an effective tool for accelerating the discovery and advancing the development of lithium-ion batteries materials. In this study, we propose a modified version of the crystal convolutional neural network (CGCNN) algorithm, namely mCGCNN. This deep learning model successfully integrates the crystal structure characteristics and the physical/chemical properties of materials, solving the data fusion problem of the current universal models in materials and significantly enhancing the efficiency and accuracy of gravimetric capacity prediction for lithium-ion batteries. In addition, the scale factors α and β are introduced to control the contribution of crystal structure and numerical data of materials to the model, which increases the flexibility and adjustability of the model. A large dataset is extracted from the Materials Project database and analyzed using various ML algorithms, including traditional ML models and deep learning models such as CGCNN and mCGCNN. In comparison to other models, mCGCNN achieves superior performance with MAE of 6.6 mAhg−1 and R2 value of 0.965 in test set. This outperforms the original CGCNN model (MAE: 42.5 mAhg−1, R2: 0.328), XGBoost model (MAE: 23.6 mAhg−1, R2: 0.842), and Random Forest model (MAE: 21.9 mAhg−1, R2: 0.844). Furthermore, the mCGCNN model is also applied to a classification task using the same dataset, achieving an AUC of 0.99 and a total accuracy of 95.5 %. The mCGCNN model not only provides a valuable tool for rapid and accurate screening of high-performance batteries using multi-source material data, but also has significant potential for the discovery of other functional materials, making it widely applicable.

Fusing modalities by multiplexed graph neural networks for outcome prediction from medical data and beyond

With the emergence of multimodal electronic health records, the evidence for diseases, events, or findings may be present across multiple modalities ranging from clinical to imaging and genomic data. Developing effective patient-tailored therapeutic guidance and outcome prediction will require fusing evidence across these modalities. Developing general-purpose frameworks capable of modeling fine-grained and multi-faceted complex interactions, both within and across modalities is an important open problem in multimodal fusion. Generalized multimodal fusion is extremely challenging as evidence for outcomes may not be uniform across all modalities, not all modality features may be relevant, or not all modalities may be present for all patients, due to which simple methods of early, late, or intermediate fusion may be inadequate. In this paper, we present a novel approach that uses the machinery of multiplexed graphs for fusion. This allows for modalities to be represented through their targeted encodings. We model their relationship between explicitly via multiplexed graphs derived from salient features in a combined latent space. We then derive a new graph neural network for multiplex graphs for task-informed reasoning. We compare our framework against several state-of-the-art approaches for multi-graph reasoning and multimodal fusion. As a sanity check on the neural network design, we evaluate the multiplexed GNN on two popular benchmark datasets, namely the AIFB and the MUTAG dataset against several state-of-the-art multi-relational GNNs for reasoning. Second, we evaluate our multiplexed framework against several state-of-the-art multimodal fusion frameworks on two large clinical datasets for two separate applications. The first is the NIH-TB portals dataset for treatment outcome prediction in Tuberculosis, and the second is the ABIDE dataset for Autism Spectrum Disorder classification. Through rigorous experimental evaluation, we demonstrate that the multiplexed GNN provides robust performance improvements in all of these diverse applications.

CSST-Net: Channel Split Spatiotemporal Network for Human Action Recognition

Temporal reasoning is crucial for action recognition tasks. The previous works use 3D CNNs to jointly capture spatiotemporal information, but it causes a lot of computational costs as well. To improve the above problems, we propose a general channel split spatiotemporal network (CSST-Net) to achieve effective spatiotemporal feature representation learning. The CSST module consists of the grouped spatiotemporal modeling (GSTM) module and the parameter-free feature fusion (PFFF) module. The GSTM module decomposes features into spatial and temporal parts along the channel dimension in parallel, which focuses on spatial and temporal clues respectively. Meanwhile, we utilize the combination of group-wise convolution and point-wise convolution to reduce the number of parameters in the temporal branch, thus alleviating the overfitting of 3D CNNs. Furthermore, for the problem of spatiotemporal feature fusion, the PFFF module performs the recalibration and fusion of spatial and temporal features by a soft attention mechanism, without introducing extra parameters, thus ensuring the correct network information flow effectively. Finally, extensive experiments on three benchmark databases (Sth-Sth V1, Sth-Sth V2, and Jester) indicate that the proposed CSST-Net can achieve competitive performance compared to existing methods, and significantly reduces the number of parameters and FLOPs of 3D CNNs baseline.

Testing for treatment effect twice using internal and external controls in clinical trials

Abstract Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given access to novel treatments. However, due to lack of randomization, RCT patients and external controls may differ with respect to covariates that may or may not have been measured. Hence, after controlling for measured covariates, for instance by matching, testing for treatment effect using external controls may still be subject to unmeasured biases. In this article, we propose a sensitivity analysis approach to quantify the magnitude of unmeasured bias that would be needed to alter the study conclusion that presumed no unmeasured biases are introduced by employing external controls. Whether leveraging external controls increases power or not depends on the interplay between sample sizes and the magnitude of treatment effect and unmeasured biases, which may be difficult to anticipate. This motivates a combined testing procedure that performs two highly correlated analyses, one with and one without external controls, with a small correction for multiple testing using the joint distribution of the two test statistics. The combined test provides a new method of sensitivity analysis designed for data fusion problems, which anchors at the unbiased analysis based on RCT only and spends a small proportion of the type I error to also test using the external controls. In this way, if leveraging external controls increases power, the power gain compared to the analysis based on RCT only can be substantial; if not, the power loss is small. The proposed method is evaluated in theory and power calculations, and applied to a real trial.

A COMPREHENSIVE MEASUREMENT MODEL FOR JOB-HOUSING BALANCE CONSIDERING SPATIAL INTERACTIONS: A CASE STUDY IN SHANGHAI

Abstract. Assessing jobs-housing balance (JHB) is crucial for the optimization of urban spatial pattern and transportation planning. However, due to the limitations of traditional data and the inability to integrate evaluation indicators, the evaluation results may be bias, conflicting with common sense. Considering interaction of multiple factors, a Job-housing balance measurement model (JHBM-SEM) was proposed to accurately evaluate the degree of JHB in a certain area. To address the problem of multi-indicator fusion, the structural equation model is used to incorporate the advantages, limitations and complementary relationships of each evaluation indicator into the evaluation process. A function relationship is defined between factor loadings, path coefficients, and variable scores to construct the measurement model and obtain comprehensive evaluation results. In addition, by integrating multi-source spatiotemporal big data mainly based on mobile signaling data, the paper can effectively mine the current status of job-housing balance in cities, and solve problems such as low precision and granularity of traditional data. The model was validated using Shanghai as an example, and the results show that compared with classical methods, the model's results are more consistent with the real situation of regional jobhousing patterns. It can identify the pseudo-balance phenomenon in underdeveloped areas of the suburbs and make reasonable evaluations, and the intermediate process can explain the direction of the imbalance factors in the region.

Robust target detection of intelligent integrated optical camera and mmWave radar system

Target detection is pivotal for modern urban computing applications. While image-based techniques are widely adopted, they falter under challenging environmental conditions such as adverse weather, poor lighting, and occlusion. To improve the target detection performance under complex real-world scenarios, this paper proposes an intelligent integrated optical camera and millimeter-wave (mmWave) radar system. Utilizing both physical knowledge and data-driven methods, a long-term robust radar-camera fusion algorithm is proposed to solve the heterogeneous data fusion problem for detection improvement. For the occlusion scenarios, the proposed algorithm can effectively detect occluded targets with the help of memory through performing long-term detection. For dark scenarios with low-light conditions, the proposed algorithm can effectively mark the target in the dark picture as well as provide rough stickman imaging. The above two innovative functions of the hybrid optical camera and mmWave radar system are tested in real-world scenarios. The results demonstrate the robustness and significant enhancement in the target detection performance of our integrated system.

Method of Cultivating College Students' Independent Learning Ability Based on Integration of Multiple Algorithm

INTRODUCTION: The research on the multi-mode fusion of college students' independent learning ability cultivation method is conducive to college students' change of learning mode and learning thinking, improvement of the utilization rate of educational resources, and the development of the academic environment as well as the reform of the educational concept.
 OBJECTIVES: Aiming at the problems of college students' current independent learning mode, such as the need for more in-depth research and the single study means.
 METHODS: A method for cultivating college students' autonomous learning ability through the integration of intelligent optimization algorithms and multiple modes has been proposed. Firstly, the practices of analyzing the current college students' autonomous learning mode and multiple learning modes are analyzed; then, using the butterfly optimization algorithm, a weight optimization method for the cultivation of college students' independent learning ability based on the fusion of multiple modes is proposed; finally, the validity and robustness of the proposed method are verified through experimental analysis.
 RESULTS: The results show that the proposed method has a high cultivation effect.
 CONCLUSION: Solves the problem of fusion of college students' independent learning ability cultivation modes.

An image fusion-based method for recovering the 3D shape of roll surface defects

Most of the existing studies on roll surface defects focus on qualitative detection and lack quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are the stylus method, laser scanning method, and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called modified multi-state pulse-coupled neural network to obtain a fully focused image. The method achieves 3D shape recovery of defects by modeling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. This method significantly improves the quality of defect detection images, with a 98% better gradient and a 28% increase in overall image quality. Additionally, it keeps 3D reconstruction errors under 4%, ensuring high accuracy and noise resistance.

Satellite image fusion using cyclic spatio-spectral GAN model

ABSTRACT Preserving spectral and spatial information in the satellite image fusion problem is one of the essential challenges. This paper presents a GAN-based method for fusing panchromatic and multispectral satellite images. The proposed method utilises the idea of Cycle-GAN with two generators, for spectral and spatial information preservation, based on residual in residual dense block super-resolution architecture. First, generator-1 translates panchromatic and multispectral satellite images to a high-resolution fused image and then preserves details by utilising generator-2. The goal is to reach the spatial and spectral details of panchromatic and multispectral satellite images, respectively. Two discriminators are employed, one for spectral and the other for spatial transformations, and weighted L1 loss is used for both as cycle losses. By leveraging the unique capabilities of the two generators, the proposed method achieves high-quality image fusion results with improved spectral and spatial resolutions that are evaluated over two different datasets. The experimental results demonstrate the effectiveness of the proposed method, with enhancements ranging from approximately 2% to 30% and 0.5% to 35% in WorldView2 and GeoEye-1 images, respectively, in five metrics including PSNR. Moreover, we show significant enhancements of approximately 7% to 50% and 22% to 29% in the same datasets in metrics like SAM and ERGAS.