High Resolution Multispectral Images Research Articles

A Three-Branch Pansharpening Network Based on Spatial and Frequency Domain Interaction

Pansharpening technology plays a crucial role in remote sensing image processing by integrating low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) images. This process addresses the limitations of satellite sensors, which cannot directly capture HRMS images. Despite significant developments achieved by deep learning-based pansharpening methods over traditional approaches, most existing techniques either fail to account for the modal differences between LRMS and PAN images, relying on direct concatenation, or use similar network structures to extract spectral and spatial information. Additionally, many methods neglect the extraction of common features between LRMS and PAN images and lack network architectures specifically designed to extract spectral features. To address these limitations, this study proposed a novel three-branch pansharpening network that leverages both spatial and frequency domain interactions, resulting in improved spectral and spatial fidelity in the fusion outputs. The proposed method was validated on three datasets, including IKONOS, WorldView-3 (WV3), and WorldView-4 (WV4). The results demonstrate that the proposed method surpasses several leading techniques, achieving superior performance in both visual quality and quantitative metrics.

Perceptual Quality Assessment for Pansharpened Images Based on Deep Feature Similarity Measure

Pan-sharpening aims to generate high-resolution (HR) multispectral (MS) images by fusing HR panchromatic (PAN) and low-resolution (LR) MS images covering the same area. However, due to the lack of real HR MS reference images, how to accurately evaluate the quality of a fused image without reference is challenging. On the one hand, most methods evaluate the quality of the fused image using the full-reference indices based on the simulated experimental data on the popular Wald’s protocol; however, this remains controversial to the full-resolution data fusion. On the other hand, existing limited no reference methods, most of which depend on manually crafted features, cannot fully capture the sensitive spatial/spectral distortions of the fused image. Therefore, this paper proposes a perceptual quality assessment method based on deep feature similarity measure. The proposed network includes spatial/spectral feature extraction and similarity measure (FESM) branch and overall evaluation network. The Siamese FESM branch extracts the spatial and spectral deep features and calculates the similarity of the corresponding pair of deep features to obtain the spatial and spectral feature parameters, and then, the overall evaluation network realizes the overall quality assessment. Moreover, we propose to quantify both the overall precision of all the training samples and the variations among different fusion methods in a batch, thereby enhancing the network’s accuracy and robustness. The proposed method was trained and tested on a large subjective evaluation dataset comprising 13,620 fused images. The experimental results suggested the effectiveness and the competitive performance.

SSTF-Unet: Spatial-Spectral Transformer-Based U-Net for High-Resolution Hyperspectral Image Acquisition.

To obtain a high-resolution hyperspectral image (HR-HSI), fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) is a prominent approach. Numerous approaches based on convolutional neural networks (CNNs) have been presented for hyperspectral image (HSI) and multispectral image (MSI) fusion. Nevertheless, these CNN-based methods may ignore the global relevant features from the input image due to the geometric limitations of convolutional kernels. To obtain more accurate fusion results, we provide a spatial-spectral transformer-based U-net (SSTF-Unet). Our SSTF-Unet can capture the association between distant features and explore the intrinsic information of images. More specifically, we use the spatial transformer block (SATB) and spectral transformer block (SETB) to calculate the spatial and spectral self-attention, respectively. Then, SATB and SETB are connected in parallel to form the spatial-spectral fusion block (SSFB). Inspired by the U-net architecture, we build up our SSTF-Unet through stacking several SSFBs for multiscale spatial-spectral feature fusion. Experimental results on public HSI datasets demonstrate that the designed SSTF-Unet achieves better performance than other existing HSI and MSI fusion approaches.

Self-supervised spectral super-resolution for a fast hyperspectral and multispectral image fusion

Hyperspectral-multispectral image fusion (HSI-MSI Fusion) for enhancing resolution of hyperspectral images is a hot topic in remote sensing. An important category of approaches for HSI-MSI Fusion is based on deep learning. The main challenges in deep learning based fusion methods include the lack of training data, poor generalization to various datasets, and high computational costs. This paper suggests a new approach to tackle these difficulties by introducing an innovative technique for HSI-MSI fusion. The proposed method involves training a tiny deep neural network that can reconstruct high-resolution hyperspectral images through spectral super-resolution of high-resolution multispectral images. This method does not require high resolution training data and they are artificially generated based on the spatial degradation model of the input observation images. Therefore, the problems of data scarcity and poor generalization are addressed, and also the computational burden is significantly reduced. After conducting thorough experiments, it was found that the proposed method provides promising results. The source code of this method is available at https://github.com/rajaei-arash/SSSR-HSI-MSI-Fusion.

Hyperspectral and multispectral images fusion based on pyramid swin transformer

Remote sensing image fusion aims to generate a high spatial resolution hyperspectral image (HR-HSI) by integrating a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI). While Convolutional Neural Networks (CNNs) have been widely employed in addressing the HSI-MSI fusion problem, their limited receptive field poses challenges in capturing global relationships within the feature maps. On the other hand, the computational complexity of Transformers hinders their application, especially in dealing with high-dimensional data like hyperspectral images (HSIs). To overcome this challenge, we propose an HSI-MSI fusion method based on the Pyramid Swin Transformer (PSTF). The pyramid design of the PSTF effectively extracts multi-scale information from images. The Spatial–Spectral Crossed Attention (SSCA) module, comprising the Cross Spatial Attention (CSA) and the Spectral Feature Integration (SFI) modules. The CSA module employs a cross-shaped self-attention mechanism, providing greater modeling flexibility for different spatial scales and non-local structures compared to traditional convolutional layers. Meanwhile, the SFI module introduces a global memory block (MB) to select the most relevant low-rank spectral vectors, integrating global spectral information with local spatial–spectral correlation to better extract and preserve spectral information. Additionally, the Separate Feature Extraction (SFE) module enhances the network’s ability to represent image features by independently processing positive and negative parts of shallow features, thus capturing details and structures more effectively and preventing the vanishing gradient problem. Compared with the state-of-the-art (SOTA) methods, experimental results demonstrate the effectiveness of the PSTF method.

R2D2-GAN: Robust Dual Discriminator Generative Adversarial Network for Microscopy Hyperspectral Image Super-Resolution.

High-resolution microscopy hyperspectral (HS) images can provide highly detailed spatial and spectral information, enabling the identification and analysis of biological tissues at a microscale level. Recently, significant efforts have been devoted to enhancing the resolution of HS images by leveraging high spatial resolution multispectral (MS) images. However, the inherent hardware constraints lead to a significant distribution gap between HS and MS images, posing challenges for image super-resolution within biomedical domains. This discrepancy may arise from various factors, including variations in camera imaging principles (e.g., snapshot and push-broom imaging), shooting positions, and the presence of noise interference. To address these challenges, we introduced a unique unsupervised super-resolution framework named R2D2-GAN. This framework utilizes a generative adversarial network (GAN) to efficiently merge the two data modalities and improve the resolution of microscopy HS images. Traditionally, supervised approaches have relied on intuitive and sensitive loss functions, such as mean squared error (MSE). Our method, trained in a real-world unsupervised setting, benefits from exploiting consistent information across the two modalities. It employs a game-theoretic strategy and dynamic adversarial loss, rather than relying solely on fixed training strategies for reconstruction loss. Furthermore, we have augmented our proposed model with a central consistency regularization (CCR) module, aiming to further enhance the robustness of the R2D2-GAN. Our experimental results show that the proposed method is accurate and robust for super-resolution images. We specifically tested our proposed method on both a real and a synthetic dataset, obtaining promising results in comparison to other state-of-the-art methods.

FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion.

The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin Transformer's self-attention mechanism for multi-scale global feature fusion. FCSwinU employs a UNet-like encoder-decoder framework to effectively merge spatiospectral features. The encoder integrates the Swin Transformer feature abstraction module (SwinTFAM) to encode pixel correlations and perform multi-scale transformations, facilitating the adaptive fusion of hyperspectral and multispectral data. The decoder then employs the Swin Transformer feature reconstruction module (SwinTFRM) to reconstruct the fused features, restoring the original image dimensions and ensuring the precise recovery of spatial and spectral details. Experimental results from three benchmark datasets and a real-world dataset robustly validate the superior performance of our method in both visual representation and quantitative assessment compared to existing fusion methods.

A novel cross fusion model with fine-grained detail reconstruction for remote sensing image pan-sharpening

ABSTRACT Pan-sharpening aims to obtain high resolution multispectral (HRMS) images by integrating the information in the panchromatic and multispectral images. Existing pan-sharpening methods have demonstrated impressive sharpening performance. However, these methods inherently overlook the complementary characteristics and interaction between diverse source images, resulting in sharpened outcomes accompanied by distortion. To solve the above problems, we construct a novel cross fusion model with fine-grained detail reconstruction from the perspective of frequency-domain. The motivation of the model is twofold: (1) to reconstruct spatial detail representations from diverse source images, laying the foundation for the generation of fine details in the subsequent fused images; and (2) to enhance the interaction between diverse source features during the fusion process in order to attain high-fidelity fusion outcomes. Based on the theoretical model, we develop a frequency-spectral dual domain cross fusion network (CF2N) utilizing the deep learning technique. Consequently, the CF2N consist of two main stages, namely frequency-domain dominated detail reconstruction (FD2R) and frequency-spectral cross fusion (FSCF). Specifically, a more reasonable reconstruction of fine frequency details in HRMS can be achieved by performing adaptive weighted fusion of frequency details in the FD2R stage. Furthermore, the FSCF module, which seamlessly integrates frequency- and spectral-domain details in a highly interactive cross fusion manner. As a result, the CF2N possesses the capability to attain high frequency-spectral fidelity results with excellent interpretability. Extensive experiments show the superior performance of ours over state of the art, while maintaining high efficiency. All implementations of this work will be published at our website.

Multispectral and hyperspectral image fusion via multi-model deep prior regularization

ABSTRACT Due to the difficulty of collecting a sufficient number of photons within a narrow spectral window, hyperspectral images often have low spatial resolution. In order to solve this problem without increasing hardware costs, a large number of image super-resolution algorithms have been proposed and have shown promising results. However, the performance of these algorithms is inconsistent in different datasets. To fix this situation, we proposed a multi-model deep prior regularization method for multispectral and hyperspectral image fusion. This method leverages mechanistic knowledge to combine various super-resolution results into a single algorithm, significantly improving the quality of the hyperspectral images. Specifically, we first proposed a variable residual mixed attention network. This network takes a low-resolution hyperspectral image and a high-resolution multispectral image as inputs, producing deep prior results from three different modes by adjusting spatial and channel attention. Then, we reconstructed the prior results along the spectral dimension and used them as regularization terms in our mechanistic model. We transformed the problem of finding the optimal solution of the model into solving the Sylvester equation and applied a fast solver for this equation to create the high-resolution hyperspectral images. Additionally, we used a genetic algorithm to determine the best hyperparameters for different deep prior results. Through validation on three datasets, we have demonstrated that our method performs better than existing methods in both quantitative and qualitative comparisons.

Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network With Spatial-Spectral Manifold Learning.

Hyperspectral image (HSI) and multispectral image (MSI) fusion aims to generate high spectral and spatial resolution hyperspectral image (HR-HSI) by fusing high-resolution multispectral image (HR-MSI) and low-resolution hyperspectral image (LR-HSI). However, existing fusion methods encounter challenges such as unknown degradation parameters, and incomplete exploitation of the correlation between high-dimensional structures and deep image features. To overcome these issues, in this article, an unsupervised blind fusion method for LR-HSI and HR-MSI based on Tucker decomposition and spatial-spectral manifold learning (DTDNML) is proposed. We design a novel deep Tucker decomposition network that maps LR-HSI and HR-MSI into a consistent feature space, achieving reconstruction through decoders with shared parameters. To better exploit and fuse spatial-spectral features in the data, we design a core tensor fusion network (CTFN) that incorporates a spatial-spectral attention mechanism for aligning and fusing features at different scales. Furthermore, to enhance the capacity to capture global information, a Laplacian-based spatial-spectral manifold constraint is introduced in shared-decoders. Sufficient experiments have validated that this method enhances the accuracy and efficiency of hyperspectral and multispectral fusion on different remote sensing datasets. The source code is available at https://github.com/Shawn-H-Wang/DTDNML.

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/.

Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications.

An enhanced chlorophyll estimation model with a canopy structural trait in maize crops: Use of multi-spectral UAV images and machine learning algorithm

Leaf chlorophyll concentration (LCC) is a key indicator of leaf nitrogen (N) and changes in canopy structure, particularly the leaf area index (LAI), play a significant role in estimating LCC. Spectral prediction models for chlorophyll are a useful tool for timely nutritional management, particularly in precision agriculture. However, the accuracy of LCC estimation is influenced by the LAI. Considering LAI data as input in the spectral prediction model is still inadequate for improving LCC estimation. This study tested the hypothesis that LCC estimate accuracy could be enhanced by using LAI as an input using high-resolution (5 cm) multi-spectral images from an unmanned aerial vehicle (UAV). For this, maize was grown as a test crop under different nutrient management in the hilly ecosystem of Meghalaya. LCC was measured using laboratory destruction methods from ground sampling that coincided with UAV flights. Machine learning algorithms such as random forest (RF), support vector machine (SVM), and kernel ridge regression (KKR) were employed to develop the LCC estimation model, utilizing band reflectance, vegetation indexes, and measured chlorophyll. The model was assessed for its sensitivity to LCC estimation using LAI data. KKR outperformed other two algorithms (RF and SVM) in accuracy of LCC estimation by >11.0 to 19.0 %. The KKR-derived LCC estimation model was significantly improved by the inclusion of LAI (R2 increased from 0.785 to 0.928 and RMSE decreased from 0.065 to 0.053 mg g−1). The model's reliability was proven on multiple UAV flights for maize crops that are healthy and nutrient-stressed. Thus, LCC models derived from multispectral UAV images using KKR algorithms could benefit the adoption of precision agriculture at field scale in mountain ecosystems.

Unsupervised Deep Tensor Network for Hyperspectral-Multispectral Image Fusion.

Fusing low-resolution (LR) hyperspectral images (HSIs) with high-resolution (HR) multispectral images (MSIs) is a significant technology to enhance the resolution of HSIs. Despite the encouraging results from deep learning (DL) in HSI-MSI fusion, there are still some issues. First, the HSI is a multidimensional signal, and the representability of current DL networks for multidimensional features has not been thoroughly investigated. Second, most DL HSI-MSI fusion networks need HR HSI ground truth for training, but it is often unavailable in reality. In this study, we integrate tensor theory with DL and propose an unsupervised deep tensor network (UDTN) for HSI-MSI fusion. We first propose a tensor filtering layer prototype and further build a coupled tensor filtering module. It jointly represents the LR HSI and HR MSI as several features revealing the principal components of spectral and spatial modes and a sharing code tensor describing the interaction among different modes. Specifically, the features on different modes are represented by the learnable filters of tensor filtering layers, the sharing code tensor is learned by a projection module, in which a co-attention is proposed to encode the LR HSI and HR MSI and then project them onto the sharing code tensor. The coupled tensor filtering module and projection module are jointly trained from the LR HSI and HR MSI in an unsupervised and end-to-end way. The latent HR HSI is inferred with the sharing code tensor, the features on spatial modes of HR MSIs, and the spectral mode of LR HSIs. Experiments on simulated and real remote-sensing datasets demonstrate the effectiveness of the proposed method.

A novel pansharpening model based on two parallel network architectures

ABSTRACT Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What’s more, these models often overlook the exploitation of PAN images’ inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network’s feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance.

Codex Climaci Rescriptus I

Abstract This article presents a fresh transcription of Codex Climaci Rescriptus I, a Christian Palestinian Aramaic manuscript from the Early Period (ca. 6th century), containing parts of Matthew’s Gospel and Mark’s Gospel. The transcription has been made from high resolution multi-spectral images. In addition, an extended introduction examines the codicology of the manuscript and its paratextual features.

Kernel-guided injection deep network for blind fusion of multispectral and panchromatic images

Deep learning (DL)-based pansharpening methods show advantages in generating a high-resolution multispectral image (MS) by fusing low-resolution MS and high-resolution panchromatic (PAN) images. However, most current research only considers a single fixed kernel in the training process, thus they may fail to handle unknown degradations by using a single model for testing, i.e., blind pansharpening. Consequently, poor performance will occur when encountering the mismatched degradation problem. Therefore, building a single network that can tackle blind pansharpening is more realistic and challenging. To this end, we design an end-to-end blind pansharpening network composed of a kernel estimation sub-network and a fusion sub-network, named BPNet. In particular, different from existing DL-based methods, we control injected details by utilizing prior degradation information produced by kernel estimation sub-network, thus it can adapt to different types of degradation. Besides, we design an effective fusion sub-network with a strong interpretability, which injects details of PAN image into MS image by a learnable injection matrix. Especially, the estimated kernel that indicates the blur level of MS image is used to update the learnable injection matrix. To preserve important features well in the deep network, channel-Transformer is further utilized as a skip-connection in the fusion sub-network. With the combination of kernel estimation and fusion sub-networks, the proposed BPNet has great advantages in complex degradation situations, as the network’s adaptability to diverse unknown degradations is enhanced. Extensive experiments on simulated and real datasets demonstrate that BPNet outperforms other state-of-the-art methods in terms of visual results and objective quality analysis.

Pansharpening Based on Multimodal Texture Correction and Adaptive Edge Detail Fusion

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (TC) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the TC and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the TC image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the TC and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics.

Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.

ReLAP-Net: Residual Learning and Attention Based Parallel Network for Hyperspectral and Multispectral Image Fusion

Remote sensing applications require high-resolution images to obtain precise information about the Earth???s surface. Multispectral images have high spatial resolution but low spectral resolution. Hyperspectral images have high spectral resolution but low spatial resolution. This study proposes a residual learning and attention-based parallel network based on residual network and channel attention. The network performs image fusion of a high spatial resolution multispectral image and a low spatial resolution hyperspectral image. The network training and fusion experiments are conducted on four public benchmark data sets to show the effectiveness of the proposed model. The fusion performance is compared with classical signal processing???based image fusion techniques. Four image metrics are used for the quantitative evaluation of the fused images. The proposed network improved fusion ability by reducing the root mean square error and relative dimensionless global error in synthesis and increased the peak signal-to-noise ratio when compared to other state-of-the-art models.