Multiplicative Iterative Nonlinear Constrained Coupled Non-negative Matrix Factorization (MINC-CNMF) for Hyperspectral and Multispectral Image Fusion

Convolutional neural networks (CNNs) have recently achieved outstanding performance for hyperspectral (HS) and multispectral (MS) image fusion. However, CNNs cannot explore the long-range dependence for HS and MS image fusion because of their local receptive fields. To overcome this limitation, a transformer is proposed to leverage the long-range dependence from the network inputs. Because of the ability of long-range modeling, the transformer overcomes the sole CNN on many tasks, whereas its use for HS and MS image fusion is still unexplored. In this article, we propose a spectral-spatial transformer (SST) to show the potentiality of transformers for HS and MS image fusion. We devise first two branches to extract spectral and spatial features in the HS and MS images by SST blocks, which can explore the spectral and spatial long-range dependence, respectively. Afterward, spectral and spatial features are fused feeding the result back to spectral and spatial branches for information interaction. Finally, the high-resolution (HR) HS image is reconstructed by dense links from all the fused features to make full use of them. The experimental analysis demonstrates the high performance of the proposed approach compared with some state-of-the-art (SOTA) methods.

The hyperspectral and multispectral image fusion process, commonly known as the multisharpening process (also known as the hypersharpening process), results in the creation of unobservable high spectral and spatial resolution hyperspectral remote sensing data, by combining (or fusing) a hyperspectral image, characterized by a high spectral resolution, with, especially, a high spatial resolution multispectral one. There are several approaches in the literature performing such a process, in particular, those based on the spectral unmixing concept. The latter fusion methods aim to extract pure-material spectra from the high spectral resolution hyperspectral image and combine them with their corresponding proportions extracted from the high spatial resolution multispectral image. However, most of these methods, which mainly consider the standard linear mixing model, do not address the spectral variability phenomenon, precisely due to the considered data mixing model, which does not handle variations in illumination and atmospheric conditions or material composition inducing spectral variability. Neglecting such a phenomenon may lead to errors in the fusion process, thus decreasing the spatial and spectral qualities of sharpened products. In this paper, a new hyperspectral and multispectral data fusion method, based on the linear spectral unmixing concept that deals with spectral variability, is proposed. This approach, based on a multiplicative Nonnegative Matrix Factorization (NMF) technique, substantially extends the multiplicative Coupled NMF (CNMF) algorithm by considering a linear data mixing model that addresses the considered phenomenon. Indeed, the introduced approach minimizes new cost functions involving matrices with specific structures in order to deal with the spectral variability phenomenon. The proposed method is applied to synthetic and real data, and its effectiveness is evaluated with established performance criteria. Experimental results show that the designed algorithm globally yields sharpened hyperspectral data with fine spectral and spatial fidelities. Also, these results demonstrate that the proposed technique globally outperforms the tested literature approaches.

Recent advances and new guidelines on hyperspectral and multispectral image fusion

Hyperspectral (HS) remote sensing image with finer spectral information has great advantages in feature identification and classification. However, the spatial resolution of HS image is usually low due to practical limitations. In this paper, the low-spatial-resolution HS image is fused with the high-spatial-resolution multispectral (MS) image of the same observation scene to improve its spatial resolution. A novel spectral unmixing based HS and MS image fusion approach (VSC-CNMF) is proposed, in which CNMF with minimum endmember simplex volume and abundance sparsity constraints is employed for coupled unmixing of HS and MS images. Simulative experiments are employed for verification and comparison. The experimental results illustrate that the newly proposed VSC-CNMF based HS and MS fusion algorithm outperforms several state-of-the-art unmixing based fusion approaches in cases with moderate number of endmembers.

Hyperspectral (HS) and multispectral (MS) image fusion aims at producing high-resolution HS (HRHS) images. However, the existing methods could not simultaneously consider the structures in both the spatial and spectral domains of the HS cube. In order to effectively preserve spatial–spectral structures in HRHS images, we propose a new low-resolution HS (LRHS) and high-resolution MS (HRMS) image fusion method based on spatial–spectral-graph-regularized low-rank tensor decomposition (SSGLRTD) in this paper. First, we reformulate the image fusion problem as a low-rank tensor decomposition model to utilize the low-rank property in the HS image. Then, two graphs are constructed in spatial and spectral domains, respectively. One of them is derived from the HRMS image for the spatial consistency, and the other is inferred from the LRHS image for spectral smoothness. Finally, the SSGLRTD fusion framework is established by combining all regularizers. With these two graphs, the spatial correlation and the spectral structure in the fused HRHS images are efficiently preserved. The experimental results on different datasets reveal that the proposed fusion method outperforms several existing fusion methods in terms of visual analysis and numerical comparison.

Abstract. Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.

Although the fusion of multispectral (MS) and hyperspectral (HS) images in remote sensing has become relatively mature, and different types of fusion methods have their own characteristics in terms of fusion effect, data dependency, and computational efficiency, few studies have focused on the impact of radiance extreme areas, which widely exist in real remotely sensed scenes. To this end, this paper proposed a novel method called radiance extreme area compensation fusion (RECF). Based on the architecture of spectral unmixing fusion, our method uses the reconstruction of error map to construct local smoothing constraints during unmixing and utilizes the nearest-neighbor multispectral data to achieve optimal replacement compensation, thereby eliminating the impact of overexposed and underexposed areas in hyperspectral data on the fusion effect. We compared the RECF method with 11 previous published methods on three sets of airborne hyperspectral datasets and HJ2 satellite hyperspectral data and quantitatively evaluated them using 5 metrics, including PSNR and SAM. On the test dataset with extreme radiance interference, the proposed RECF method achieved well in the overall evaluation results; for instance, the PSNR metric reached 47.6076 and SAM reached 0.5964 on the Xiong’an dataset. In addition, the result shows that our method also achieved better visual effects on both simulation and real datasets.

In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity and multimodal characteristics. Diffusion models, known for their stable training process and exceptional image generation capabilities, have shown good application potential in this field. However, when dealing with multimodal data, it may prove challenging for the models to fully capture the intricate relationships between the modalities, which may result in incomplete information integration and a small amount of remaining noise in the generated images. To address these problems, we propose a new model, KanDiff, for hyperspectral and multispectral image fusion. To address the differences in modal information between multispectral and hyperspectral images, KANDiff incorporates Kolmogorov–Arnold Networks (KAN) to guide the inputs. It helps the model understand the complex relationships between the modalities by replacing the fixed activation function in the traditional MLP with a learnable activation function. Furthermore, the image generated by the diffusion model may exhibit a small amount of the remaining noise. Convolutional Neural Networks (CNNs) effectively extract local features through their convolutional layers and achieve noise suppression via layer-by-layer feature representation. Therefore, the MergeCNN module is further introduced to enhance the fusion effect, resulting in smoother and more accurate outcomes. Experimental results on the public CAVE and Harvard datasets indicate that KanDiff has achieved improvements over current high-performance methods across several metrics, particularly showing significant enhancements in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM), thus demonstrating superior performance. Additionally, we have created an image fusion dataset of the lunar surface, and KANDiff exhibits robust performance on this dataset as well. This work introduces an effective solution for addressing the challenges posed by missing high-resolution hyperspectral images (HRHS) data, which is essential for tasks including landing site selection and resource exploration within the realm of deep space exploration.

The fusion of low-resolution hyperspectral image (LR-HSI) and high-resolution multispectral image (HR-MSI) is a crucial technology for producing high-resolution hyperspectral images. Most existing image fusion algorithms based on deep learning do not fully utilize the ability of neural network to extract and process multi-scale features, which leads to the problem of difficulty in fully learning features and ambiguity of features. In order to overcome these issues, a residual selective kernel attention-based U-net named RSKAU-net is designed for LR-HSI and HR-MSI fusion. RSKAU-net is constructed by a residual selective kernel module with an attention mechanism and a channel attention block. The residual selective kernel attention-based (RSKA) module is designed to process images of different resolutions, which adaptively extracts multi-scale features and efficiently emphasizes significant features through the attention mechanism. The channel attention (CA) module retains important spectral information by assigning different weights to each channel of LR-HSI. The proposed network can enhance the spatial information of LR-HSI while preserving its spectral information. Meanwhile, it effectively fuses the features from the source image to obtain the HR-HSI with rich details. The experimental results demonstrate that the proposed network has advantages in terms of both visual effect and objective quantitative indices when compared to existing HSI-MSI fusion approaches.

Hyperspectral-multispectral image fusion aims to generate high-resolution hyperspectral (HRHS) images with rich spectral details, by mining the complementary information between low-resolution hyperspectral (LRHS) images and high resolution multispectral (HRMS) images. However, in practical scenarios, LRHS images are susceptible to noise interference due to their narrow spectral bandwidth, resulting in limited energy absorption of surface reflectance in each band. Various noise disrupt the correlations of LRHS and HRMS images, thus leading to the spatial details loss or spectral distortion. To address these challenges, we propose an anti-noise hyperspectral and multispectral image fusion network based on State Space Model (SSM), namely AHMFNet. To mitigate noise in LRHS images, the spectral denoising and refinement module is designed by combining low-rank representation with the discrete wavelet transform, while simultaneously extracting key spectral features. Then, the SSM is utilized for global modeling of spectral information to enhance essential features. Finally, although spectral and spatial features have differences, the spatial-spectral cross Mamba is designed to fuse them by mining their relationships, thus improving the fusion quality. Experiments on four public datasets show that, compared to existing state-of-the-art methods, the proposed AHMFNet generates images with superior spatial quality and more accurate spectral characteristics under various noise conditions.

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN) images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this study, we propose an interpretable model-driven deep network for HS, MS, and PAN image fusion, called HMPNet. We first propose a new fusion model that utilizes a deep before describing the complicated relationship between the HRHS and PAN images owing to their large resolution difference. Consequently, the difficulty of traditional model-based approaches in designing suitable hand-crafted priors can be alleviated because this deep prior is learned from data. We further solve the optimization problem of this fusion model based on the proximal gradient descent (PGD) algorithm, achieved by a series of iterative steps. By unrolling these iterative steps into several network modules, we finally obtain the HMPNet. Therefore, all parameters besides the deep prior are learned in the deep network, simplifying the selection of optimal parameters in the fusion and achieving a favorable equilibrium between the spatial and spectral qualities. Meanwhile, all modules contained in the HMPNet have explainable physical meanings, which can improve its generalization capability. In the experiment, we exhibit the advantages of the HMPNet over other state-of-the-art methods from the aspects of visual comparison and quantitative analysis, where a series of simulated as well as real datasets are utilized for validation.

Due to the difficulties of directly obtaining high-resolution hyperspectral images (HR-HSI), the fusion of low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI) has emerged as an effective approach. While existing methods leverage image-level priors from HR-MSI, they often lack explicit semantic guidance for precise detail reconstruction. Recognizing that textual scene descriptions encapsulate valuable object attributes and contextual information, we introduce the first Language-Bridging framework for Hyperspectral and Multispectral image fusion (CO²IF). CO²IF leverages language semantics as prior knowledge to explicitly guide the reconstruction process. To bridge the modality gap between textual descriptions and high-dimensional hyperspectral data, we design a Cross-modal Optimal Transport (COT) module. COT establishes precise semantic correspondences between language features and the visual cues of individual spectral bands. Building upon this semantic alignment, we develop a Multimodal Coordinated State Space Model (CoMamba). CoMamba effectively integrates the language-derived priors with spatial information from HR-MSI and spectral information from LR-HSI. This language-guided reconstruction significantly enhances the extraction of crucial spatial-spectral details, leading to superior fidelity in the generated HR-HSI. In addition, this paper adds text descriptions for three widely used datasets. Both qualitative and quantitative experimental results on the public datasets confirm the superiority of the proposed method compared to the SOTA methods.

Hyperspectral and multispectral image fusion plays an important role in the fields such as image restoration, space robotics, remote sensing, computer vision, etc. The fused images take advantages of the high spectral resolutions of hyperspectral images and the high spatial resolutions of multispectral images. So there has been a recent outgrowth of interests of research in this field. However, the local structural image regularity and non-local image self-similarity are not yet explored in existing fusion methods. This paper develops a novel method for fusing hyperspectral and multispectral images with non-local structure tensor total variation regularization. Specifically, the weight function to measure self-similarity in non-local domain is introduced on the basis of the definition of structure tensor. Therefore, the proposed method can exploit the regularity and self-similarity of spatial-spectral image’s non-local structures simultaneously. In addition to the general formulation of non-local structure tensor variation, this paper presents the discrete non-local structure tensor variation for hyperspectral and multispectral image fusion whose measurements are discrete. This method can provide an alternative way to measure the image variation of multi-band image. The resulting problem is solved by alternating direction method of multipliers (ADMM), a simple but powerful algorithm for convex optimization problems. The optimization problem is handled by a splitting variables strategy, which is implemented by introducing four auxiliary variables regarding to the required fused image. The iteration related to the proposed non-local structure tensor regularization term is solved by the proximal mapping method. The solutions of other iterations are given through fast Fourier transforms. Extensive computational experiments are performed on various datasets. The results of index evaluation demonstrate the effectiveness of the proposed method. These results also indicate non-local structure tensor total variation regularization has obvious advantages over the state-of-the-art fusion methods.KeywordsImage fusionNon-local tensor regularizationOptimization method

<strong class="journal-contentHeaderColor">Abstract.</strong> In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons; first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community with a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7 May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can serve as baselines for further studies. The dataset is publicly available at <span class="uri">https://doi.org/10.14459/2022mp1657312</span> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx26">Hu et al.</a>, <a href="#bib1.bibx26">2022</a><a href="#bib1.bibx26">a</a>)</span> and the code (including the pre-trained models) at <span class="uri">https://doi.org/10.5281/zenodo.7428215</span> <span class="cit" id="xref_paren.2">(<a href="#bib1.bibx27">Hu et al.</a>, <a href="#bib1.bibx27">2022</a><a href="#bib1.bibx27">b</a>)</span>.

This paper studies a new Bayesian optimization algorithm for fusing hyperspectral and multispectral images. The hyperspectral image is supposed to be obtained by blurring and subsampling a high spatial and high spectral target image. The multispectral image is modeled as a spectral mixing version of the target image. By introducing appropriate priors for parameters and hyperparameters, the fusion problem is formulated within a Bayesian estimation framework, which is very convenient to model the noise and the target image. The high spatial resolution hyperspectral image is then inferred from its posterior distribution. To compute the Bayesian maximum a posteriori estimator associated with this posterior, an alternating direction method of multipliers within block coordinate descent algorithm is proposed. Simulation results demonstrate the efficiency of the proposed fusion method when compared with several state-of-the-art fusion techniques.