Low Resolution Multispectral Image 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.

Unsupervised Pan-Sharpening via Mutually Guided Detail Restoration

Pan-sharpening is a task that aims to super-resolve the low-resolution multispectral (LRMS) image with the guidance of a corresponding high-resolution panchromatic (PAN) image. The key challenge in pan-sharpening is to accurately modeling the relationship between the MS and PAN images. While supervised deep learning methods are commonly employed to address this task, the unavailability of ground-truth severely limits their effectiveness. In this paper, we propose a mutually guided detail restoration method for unsupervised pan-sharpening. Specifically, we treat pan-sharpening as a blind image deblurring task, in which the blur kernel can be estimated by a CNN. Constrained by the blur kernel, the pan-sharpened image retains spectral information consistent with the LRMS image. Once the pan-sharpened image is obtained, the PAN image is blurred using a pre-defined blur operator. The pan-sharpened image, in turn, is used to guide the detail restoration of the blurred PAN image. By leveraging the mutual guidance between MS and PAN images, the pan-sharpening network can implicitly learn the spatial relationship between the two modalities. Extensive experiments show that the proposed method significantly outperforms existing unsupervised pan-sharpening methods.

Triple disentangled network with dual attention for remote sensing image fusion

Remote sensing applications, such as detection and recognition of objects, need a large number of high spatial resolution (HR) images. As a feasible technique, image fusion is considered to produce HR images. However, most of remote sensing image fusion methods based on deep neural network (DNN) are mainly limited by the extraction of spatial and spectral features. These methods ignore the redundancy among these features, which causes spatial and spectral distortions in the fused image. In this paper, we propose a novel image fusion method based on a triple disentangled network (TDNet) with dual attention to reduce the redundancy in the extracted features. In the proposed method, it is assumed that the information in panchromatic (PAN) and low spatial resolution multispectral (LRMS) images can be encoded as the spatial, spectral, and common features. Specifically, we construct a triple-stream network to extract these features. To efficiently model the spatial and spectral information in PAN and LRMS images, local–global attention and interdependency attention are designed and integrated into the network. Then, the redundancy among these features is reduced by disentangled learning, in which these features are recombined to reconstruct the PAN and LRMS images. Besides, these features should complement each other. So, we utilize the maximal coding rate reduction to balance the redundancy and complementarity among them. Finally, all features are recombined to synthesize the high spatial resolution multispectral image. The experimental results demonstrate that the proposed TDNet has a superior performance in terms of qualitative and quantitative evaluations. The code link is https://github.com/RSMagneto/TDNet.

Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet)

Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods’ pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology’s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality.

ZeRGAN: Zero-Reference GAN for Fusion of Multispectral and Panchromatic Images.

In this article, we present a new pansharpening method, a zero-reference generative adversarial network (ZeRGAN), which fuses low spatial resolution multispectral (LR MS) and high spatial resolution panchromatic (PAN) images. In the proposed method, zero-reference indicates that it does not require paired reduced-scale images or unpaired full-scale images for training. To obtain accurate fusion results, we establish an adversarial game between a set of multiscale generators and their corresponding discriminators. Through multiscale generators, the fused high spatial resolution MS (HR MS) images are progressively produced from LR MS and PAN images, while the discriminators aim to distinguish the differences of spatial information between the HR MS images and the PAN images. In other words, the HR MS images are generated from LR MS and PAN images after the optimization of ZeRGAN. Furthermore, we construct a nonreference loss function, including an adversarial loss, spatial and spectral reconstruction losses, a spatial enhancement loss, and an average constancy loss. Through the minimization of the total loss, the spatial details in the HR MS images can be enhanced efficiently. Extensive experiments are implemented on datasets acquired by different satellites. The results demonstrate that the effectiveness of the proposed method compared with the state-of-the-art methods. The source code is publicly available at https://github.com/RSMagneto/ZeRGAN.

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.

GDF: A Novel Image Fusion Approach for Compelling Depiction of Earthly Features

One of the most challenging aspects of satellite remote sensing is image fusion. Image fusion increases the visual interpretation of the image and has many applications such as monitoring water bodies, land cover, urbanisation, agriculture, national defence, and so forth. Remote sensing applications require images with a high spatial and spectral resolution for accurately processing and distinguishing the land cover classes with fine texture and shape details. Due to technical limitations, most satellites cannot take high-resolution multi-spectral images but can get high-resolution panchromatic images and low-resolution multi-spectral satellite images separately. This article proposes a novel fusion method, geospatial data fusion (GDF), to obtain high-resolution multi-spectral images. GDF, along with three well-known fusion methods viz., Brovey Transform (BT), wavelet transform (WT), and Fourier transform (FT), have been comparatively implemented to fuse the Cartosat-2 and Sentinel-2 imageries of the Sangam area of Prayagraj, India. The fusion has been done to extract the earth’s surface features from the fused imagery. In this research, the fused image is utilised for river water mapping. Results confirm that the GDF outperforms other existing fusion methods and successfully maps the river water in the study area.

Zero-shot semi-supervised learning for pansharpening

Pansharpening refers to fusing a low-resolution multispectral image (LRMS) and a high-resolution panchromatic (PAN) image to generate a high-resolution multispectral image (HRMS). Traditional pansharpening methods use a single pair of LRMS and PAN to generate HRMS at full resolution, but they fail to generate high-quality fused products due to the assumption of a (often inaccurate) linear relationship between the fused products. Convolutional neural network methods, i.e., supervised and unsupervised learning approaches, can model any arbitrary non-linear relationship among data, but performing even worse than traditional methods when testing data are not consistent with training data. Moreover, supervised methods rely on simulating reduced resolution data for training causing information loss at full resolution. Unsupervised pansharpening suffers from distortion due to the lack of reference images and inaccuracy in the estimation of the degradation process. In this paper, we propose a zero-shot semi-supervised method for pansharpening (ZS-Pan), which only requires a single pair of PAN/LRMS images for training and testing networks combining both the pros of supervised and unsupervised methods. Facing with challenges of limited training data and no reference images, the ZS-Pan framework is built with a two-phase three-component model, i.e., the reduced resolution supervised pre-training (RSP), the spatial degradation establishment (SDE), and the full resolution unsupervised generation (FUG) stages. Specifically, a special parameter initialization technique, a data augmentation strategy, and a non-linear degradation network are proposed to improve the representation ability of the network. In our experiments, we evaluate the performance of the proposed framework on different datasets using some state-of-the-art (SOTA) pansharpening approaches for comparison. Results show that our ZS-Pan outperforms these SOTA methods, both visually and quantitatively. The code is available at https://github.com/coder-qicao/ZS-Pan.

Zero-Sharpen: A universal pansharpening method across satellites for reducing scale-variance gap via zero-shot variation

Pansharpening is a technique that combines a high-resolution panchromatic image (HRPAN) and a low-resolution multi-spectral image (LRMS) to generate a high-resolution multi-spectral image (HRMS). Traditional methods perform sharpening based on given image pairs, but their performance is limited due to the employment of scale-varying linear mapping assumptions. Existing deep-learning-based methods can establish arbitrary non-linear sharpening functions based on large-scale training data. However, supervised methods suffer from scale-variance generalization for training on the simulated reduced resolution data, while unsupervised methods arise distortion for the absence of reference and introduction of inaccurate spectral observation assumptions. Besides, the generic satellite-specific learning (i.e. training and testing on the homologous satellite data) causes low generalization when processing the heterologous satellite data. To this end, we combine the advantages of deep learning and variational optimization, to propose a universal pansharpening method that can be applied across different satellites with reducing the scale variance, termed as Zero-Sharpen. On the one hand, we build image-pair-specific neural networks to extend the spatial mapping and spectral observation assumptions to the nonlinear space. These assumptions are incorporated as deep spatial prior and deep spectral observation prior for regularization, assisting variational models to iteratively adapt to the full-resolution scale. On the other hand, the variational optimization mechanism also promotes the optimization of deep networks so as to achieve the preservation of spectral and spatial information in a zero-shot learning manner. In doing so, our method can be easily available across different satellites. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods both qualitatively and quantitatively. Code is publicly available at https://github.com/Baixuzx7/ZeroSharpen.

Pan-sharpening via conditional invertible neural network

In the realm of conventional deep-learning-based pan-sharpening approaches, there has been an ongoing struggle to harmonize the input panchromatic (PAN) and multi-spectral (MS) images across varied channels. Existing methods have often been stymied by spectral distortion and an inadequate texture representation. To address these limitations, we present an innovative constraint-based image generation strategy tailored for the pan-sharpening task. Our method employs a multi-scale conditional invertible neural network, named PSCINN, which is capable of converting the ground truth MS image into a downscaled MS image and a latent variable, all under the guidance of the PAN image. Subsequently, the resampled latent variable, obtained from a prior distribution, and the low-resolution MS image are harnessed to predict the pan-sharpened image in an information-preserving manner, with the PAN image providing essential guidance during the reversion process. Furthermore, we meticulously architect a conditional invertible block to construct a Jacobian Determinant for the spectral information recovery. This structure effectively pre-processes the conditioning PAN image into practical texture information, thereby preventing the spectral information in the pan-sharpened result from potential contamination. The proposed PSCINN outperforms existing state-of-the-art pan-sharpening methodologies, both in terms of objective and subjective results. Post-treatment experiments underscore a substantial enhancement in the perceived quality attributed to our method. The source code for PSCINN will be accessible at https://github.com/jiaming-wang/PSCINN.

Multispectral and hyperspectral image fusion based on low-rank unfolding network

Recently, deep unfolding networks (DUNs) have been applied to the fusion of low spatial resolution hyperspectral (LR HS) and high spatial resolution multispectral (HR MS) images and achieved satisfactory high spatial resolution hyperspectral (HR HS) images. However, the low-rank and sparse priors in these networks are not exploited sufficiently. In this paper, we establish an LR HS and HR MS image fusion model based on robust principal component analysis (RPCA), which simultaneously captures the low-rank and sparse properties in HS images. Then, the fusion model is optimized with the alternating direction method of multipliers (ADMM). To make full use of the representation capacity of DUNs, we unfold the derived ADMM algorithm as a network, named low-rank unfolding network (LRU-Net). Specifically, each iteration in ADMM is unfolded as one stage in LRU-Net, in which the low-rank and sparse priors are learned by singular value thresholding (SVT) and sparse module, respectively. Finally, all features from all stages are integrated to produce the desired HR HS image. Three benchmark datasets were chosen for comparison to demonstrate the effectiveness of the proposed LRU-Net. The experimental results demonstrate that LRU-Net performs better in terms of both qualitative and quantitative results compared to state-of-the-art fusion methods. The source code is publicly available at https://github.com/RSMagneto/LRU-Net.

A pan-sharpening network using multi-resolution transformer and two-stage feature fusion.

Pan-sharpening is a fundamental and crucial task in the remote sensing image processing field, which generates a high-resolution multi-spectral image by fusing a low-resolution multi-spectral image and a high-resolution panchromatic image. Recently, deep learning techniques have shown competitive results in pan-sharpening. However, diverse features in the multi-spectral and panchromatic images are not fully extracted and exploited in existing deep learning methods, which leads to information loss in the pan-sharpening process. To solve this problem, a novel pan-sharpening method based on multi-resolution transformer and two-stage feature fusion is proposed in this article. Specifically, a transformer-based multi-resolution feature extractor is designed to extract diverse image features. Then, to fully exploit features with different content and characteristics, a two-stage feature fusion strategy is adopted. In the first stage, a multi-resolution fusion module is proposed to fuse multi-spectral and panchromatic features at each scale. In the second stage, a shallow-deep fusion module is proposed to fuse shallow and deep features for detail generation. Experiments over QuickBird and WorldView-3 datasets demonstrate that the proposed method outperforms current state-of-the-art approaches visually and quantitatively with fewer parameters. Moreover, the ablation study and feature map analysis also prove the effectiveness of the transformer-based multi-resolution feature extractor and the two-stage fusion scheme.

A image fusion and U-Net approach to improving crop planting structure multi-category classification in irrigated area

Some subtle features of planting structures in irrigation areas could only be visible on high-resolution panchromatic spectral images. However, low spatial resolution multispectral image makes it hard to recognize them. It is challenging to accurately obtain crop planting structure when using traditional methods. This paper proposes an extraction method of crop planting structure based on image fusion and U-Net depth semantic segmentation network, which can automatically and accurately extract multi-category crop planting structure information. This method takes Landsat8 commercial multispectral satellite data set as an example, chooses RGB pseudo-color synthetic image which highlights vegetation characteristics, and uses HLS(Hue, Luminance, Saturation), NND(Nearest-Neighbor Diffusion) and G-S(Gram-Schmidt) methods to fuse panchromatic band to obtain 15m high-resolution fusion image to obtain training set and test set, six types of land features including cities and rivers were labeled by manual to obtain the verification set. The training and validation sets are cut and enhanced to train the U-Net semantic segmentation network. Taking the Xiaokaihe irrigation area in Binzhou City, Shandong Province, China, as an example, the planting structure was classified, and the overall accuracy was 87.7%, 91.2%, and 91.3%, respectively. The accuracy of crop planting structures (wheat, cotton, woodland) was 74.2%, 82.5%, 82.3%, and the Kappa coefficient was 0.832, 0.880, and 0.881, respectively. The results showed that the NND-UNet method was suitable for large-scale continuous crop types (wheat, cotton), and the GS-UNet method had a better classification effect in discrete areas of cash crops (Jujube and many kinds of fruit trees).

Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening

Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are generated by applying transform operation to the features of LRMS images and PAN images, which preserve the spatial location information. Then, the dynamic routing algorithm is modified to adaptively select the lower-level capsules to generate the higher-level capsule features to represent the fusion of LRMS images and PAN images, which can effectively avoid the loss of detailed information. In addition, the previous addition and concatenation operations are illustrated as special cases of our MDRL. Based on MIPSM with addition operations and DRPNN with concatenation operations, two modified dynamic routing models named MDR–MIPSM and MDR–DRPNN are further proposed for pan-sharpening. Extensive experimental results demonstrate that the proposed method can achieve remarkable spectral and spatial quality.

RaGMAN: A relativistic average generative multi-adversarial network for pansharpening

Pansharpening aims to reconstruct a high-resolution multi-spectral image (HRMS) from a low spatial resolution multi-spectral (MS) image and a high spatial resolution single band panchromatic (PAN) image acquired from the same satellite. To solve the spatial information and spectral information of the fused image that are not evenly preserved, this paper proposes a relativistic average generative multi-adversarial network (RaGMAN) for pansharpening. The RaGMAN consists of two parts. One builds a two-stream generator network to generate HRMS images by extracting features from PAN and MS images. The other is a dual discriminator to preserve the spectral and spatial information of the input when performing fusion. To make the generated image contain more spatial information, we propose multiple residual dense block in the generator. At the same time, in order to improve the overall quality of the fused images, two relativistic average discriminators are used in the network. Furthermore, a novel hybrid loss function is introduced to optimize training. Compared with seven state-of-the-art methods on GF-2, QB and WV-3 datasets, experimental results show that the proposed RaGMAN method can produce excellent pansharpening performance in terms of spatial and spectral fidelity.

Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules

The purpose of the panchromatic sharpening of remote sensing images is to generate high-resolution multispectral images through software technology without increasing economic expenditure. The specific method is to fuse the spatial information of a high-resolution panchromatic image and the spectral information of a low-resolution multispectral image. This work proposes a novel model for generating high-quality multispectral images. This model uses the feature domain of the convolution neural network to fuse multispectral and panchromatic images so that the fused images can generate new features so that the final fused features can restore clear images. Because of the unique feature extraction ability of convolution neural networks, we use the core idea of convolution neural networks to extract global features. To extract the complementary features of the input image at a deeper level, we first designed two subnetworks with the same structure but different weights, and then used single-channel attention to optimize the fused features to improve the final fusion performance. We select the public data set widely used in this field to verify the validity of the model. The experimental results on the GaoFen-2 and SPOT6 data sets show that this method has a better effect in fusing multi-spectral and panchromatic images. Compared with the classical and the latest methods in this field, our model fusion obtained panchromatic sharpened images from both quantitative and qualitative analysis has achieved better results. In addition, to verify the transferability and generalization of our proposed model, we directly apply it to multispectral image sharpening, such as hyperspectral image sharpening. Experiments and tests have been carried out on Pavia Center and Botswana public hyperspectral data sets, and the results show that the model has also achieved good performance in hyperspectral data sets.

DICO: Dingo coot optimization-based ZF net for pansharpening

With the recent advancements in technology, there has been a tremendous growth in the usage of images captured using satellites in various applications, like defense, academics, resource exploration, land-use mapping, and so on. Certain mission-critical applications need images of higher visual quality, but the images captured by the sensors normally suffer from a tradeoff between high spectral and spatial resolutions. Hence, for obtaining images with high visual quality, it is necessary to combine the low resolution multispectral (MS) image with the high resolution panchromatic (PAN) image, and this is accomplished by means of pansharpening. In this paper, an efficient pansharpening technique is devised by using a hybrid optimized deep learning network. Zeiler and Fergus network (ZF Net) is utilized for performing the fusion of the sharpened and upsampled MS image with the PAN image. A novel Dingo coot (DICO) optimization is created for updating the learning parameters and weights of the ZF Net. Moreover, the devised DICO_ZF Net for pansharpening is examined for its effectiveness by considering measures, like Peak Signal To Noise Ratio (PSNR) and Degree of Distortion (DD) and is found to have attained values at 50.177 dB and 0.063 dB.

Enhanced Deep Blind Hyperspectral Image Fusion.

The goal of hyperspectral image fusion (HIF) is to reconstruct high spatial resolution hyperspectral images (HR-HSI) via fusing low spatial resolution hyperspectral images (LR-HSI) and high spatial resolution multispectral images (HR-MSI) without loss of spatial and spectral information. Most existing HIF methods are designed based on the assumption that the observation models are known, which is unrealistic in many scenarios. To address this blind HIF problem, we propose a deep learning-based method that optimizes the observation model and fusion processes iteratively and alternatively during the reconstruction to enforce bidirectional data consistency, which leads to better spatial and spectral accuracy. However, general deep neural network inherently suffers from information loss, preventing us to achieve this bidirectional data consistency. To settle this problem, we enhance the blind HIF algorithm by making part of the deep neural network invertible via applying a slightly modified spectral normalization to the weights of the network. Furthermore, in order to reduce spatial distortion and feature redundancy, we introduce a Content-Aware ReAssembly of FEatures module and an SE-ResBlock model to our network. The former module helps to boost the fusion performance, while the latter make our model more compact. Experiments demonstrate that our model performs favorably against compared methods in terms of both nonblind HIF fusion and semiblind HIF fusion.

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.

Panchromatic and Multispectral Image Fusion Combining GIHS, NSST, and PCA

Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fusion methods—component substitution (CS) and multi-resolution analysis (MRA), which have different advantages—mixed approaches are possible. This paper proposes a fusion algorithm that combines the advantages of generalized intensity–hue–saturation (GIHS) and non-subsampled shearlet transform (NSST) with principal component analysis (PCA) technology to extract more spatial information. Therefore, compared with the traditional algorithms, the algorithm in this paper uses PCA transformation to obtain spatial structure components from PAN and MS, which can effectively inject spatial information while maintaining spectral information with high fidelity. First, PCA is applied to each band of low-resolution multispectral (MS) images and panchromatic (PAN) images to obtain the first principal component and to calculate the intensity of MS. Then, the PAN image is fused with the first principal component using NSST, and the fused image is used to replace the original intensity component. Finally, a fused image is obtained using the GIHS algorithm. Using the urban, plants and water, farmland, and desert images from GeoEye-1, WorldView-4, GaoFen-7 (GF-7), and Gaofen Multi-Mode (GFDM) as experimental data, this fusion method was tested using the evaluation mode with references and the evaluation mode without references and was compared with five other classic fusion algorithms. The results showed that the algorithms in this paper had better fusion performances in both spectral preservation and spatial information incorporation.