Pan-sharpening Model Research Articles

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.

An anisotropic variational pansharpening model with adaptive coefficients

An anisotropic variational pansharpening model with adaptive coefficients

An Interpretable Unsupervised Unrolling Network for Hyperspectral Pansharpening.

Existing deep convolutional neural networks (CNNs) have recently achieved great success in pansharpening. However, most deep CNN-based pansharpening models are based on "black-box" architecture and require supervision, making these methods rely heavily on the ground-truth data and lose their interpretability for specific problems during network training. This study proposes a novel interpretable unsupervised end-to-end pansharpening network, called as IU2PNet, which explicitly encodes the well-studied pansharpening observation model into an unsupervised unrolling iterative adversarial network. Specifically, we first design a pansharpening model, whose iterative process can be computed by the half-quadratic splitting algorithm. Then, the iterative steps are unfolded into a deep interpretable iterative generative dual adversarial network (iGDANet). Generator in iGDANet is interwoven by multiple deep feature pyramid denoising modules and deep interpretable convolutional reconstruction modules. In each iteration, the generator establishes an adversarial game with the spatial and spectral discriminators to update both spectral and spatial information without ground-truth images. Extensive experiments show that, compared with the state-of-the-art methods, our proposed IU2PNet exhibits very competitive performance in terms of quantitative evaluation metrics and qualitative visual effects.

A Swin Transformer with Dynamic High-Pass Preservation for Remote Sensing Image Pansharpening

Pansharpening is a technique used in remote sensing to combine high-resolution panchromatic (PAN) images with lower resolution multispectral (MS) images to generate high-resolution multispectral images while preserving spectral characteristics. Recently, convolutional neural networks (CNNs) have been the mainstream in pansharpening by extracting the deep features of PAN and MS images and fusing these abstract features to reconstruct high-resolution details. However, they are limited by the short-range contextual dependencies of convolution operations. Although transformer models can alleviate this problem, they still suffer from weak capability in reconstructing high-resolution detailed information from global representations. To this end, a novel Swin-transformer-based pansharpening model named SwinPAN is proposed. Specifically, a detail reconstruction network (DRNet) is developed in an image difference and residual learning framework to reconstruct the high-resolution detailed information from the original images. DRNet is developed based on the Swin Transformer with a dynamic high-pass preservation module with adaptive convolution kernels. The experimental results on three remote sensing datasets with different sensors demonstrate that the proposed approach performs better than state-of-the-art networks through qualitative and quantitative analysis. Specifically, the generated pansharpening results contain finer spatial details and richer spectral information than other methods.

CMVFTA: Optimal regression and deep maxout with optimization algorithm for pan-sharpening

Pan-sharpening is a procedure to fuse the spatial detail of high-resolution multispectral images (HR-MSI) and low-resolution hyperspectral images (LR-HSI) to produce HR-MSI. Due to increase in high-resolution satellites, methods based on pan-sharpening are increasingly utilized all over the world. However, the majority of techniques consider pan-sharpening as a major issue, which hinders the discriminative ability. This work proposes an optimization-based deep model for pan-sharpening using LR-HSI and HR-MSI. Initially, the LR-HSI is input into an up-sampling mode, and the resulting image is fed into weighted linear regression. Concurrently, HR-MSI is supplied into weighted linear regression. Weighted linear regression is used to combine the upsampled LR-HSI and HR-MSI. The HR-MSI is then sent into the Deep Maxout network (DMN), which learns the priors via residual learning. Furthermore, the suggested Competitive Multi-Verse Feedback Artificial Tree (CMVFTA) strategy is used for DMN training, which is constructed by combining the Competitive Multi-Verse Optimizer (CMVO) and Feedback Artificial Tree (FAT) approaches. Finally, the DMN, LR-HSI, and HR-MSI outputs are merged together to provide a pan-sharpening image. The proposed CMVFTA-based DMN offered enhanced performance with Degree of Distortion (DD) of 0.0402 dB, Peak signal-to-noise ratio (PSNR) of 49.60 dB, Root Mean Squared Error (RMSE) of 0.330, Relative Average Spectral Error (RASE) of 0.322, Filtered Correlation Coefficients (FCC) of 0.874, Quality with no reference (QNR) of 76.19.

Swin–MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN

Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs a three-stream pan-sharpening network based on the Swin transformer and a multi-scale feature extraction module. Unlike the traditional convolutional neural network (CNN) pan-sharpening model, we use the Swin transformer to establish global connections with the image and combine it with a multi-scale feature extraction module to extract local features of different sizes. The model combines the advantages of the Swin transformer and CNN, enabling fused images to maintain good local detail and global linkage by mitigating distortion in hyperspectral images. In order to verify the effectiveness of the method, this paper evaluates fused images with subjective visual and quantitative indicators. Experimental results show that the method proposed in this paper can better preserve the spatial and spectral information of images compared to the classical and latest models.

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

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

W-NetPan: Double-U network for inter-sensor self-supervised pan-sharpening

The increasing availability of remote sensing data allows dealing with spatial-spectral limitations by means of pan-sharpening methods. However, fusing inter-sensor data poses important challenges, in terms of resolution differences, sensor-dependent deformations and ground-truth data availability, that demand more accurate pan-sharpening solutions. In response, this paper proposes a novel deep learning-based pan-sharpening model which is termed as the double-U network for self-supervised pan-sharpening (W-NetPan). In more details, the proposed architecture adopts an innovative W-shape that integrates two U-Net segments which sequentially work for spatially matching and fusing inter-sensor multi-modal data. In this way, a synergic effect is produced where the first segment resolves inter-sensor deviations while stimulating the second one to achieve a more accurate data fusion. Additionally, a joint loss formulation is proposed for effectively training the proposed model without external data supervision. The experimental comparison, conducted over four coupled Sentinel-2 and Sentinel-3 datasets, reveals the advantages of W-NetPan with respect to several of the most important state-of-the-art pan-sharpening methods available in the literature. The codes related to this paper will be available at https://github.com/rufernan/WNetPan.

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

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

Retracted: Artificial Intelligence-Based Deep Fusion Model for Pan-Sharpening of Remote Sensing Images.

[This retracts the article DOI: 10.1155/2021/7615106.].

An Adaptive Injection Model for Pansharpening.

Pansharpening technology is used to acquire a multispectral image with high spatial resolution from a panchromatic (PAN) image and a multispectral (MS) image. The detail injection model is popular for its flexibility. However, the accuracy of the injection gain and the extracted details may greatly influence the quality of the pansharpened image. This paper proposes an adaptive injection model to solve these problems. For detail extraction, we present a Gaussian filter estimation algorithm by exploring the intrinsic character of the MS sensor and convolving the PAN image with the filter to adaptively optimize the details to be consistent with the character of the MS image. For the adaptive injection coefficient, we iteratively adjust the coefficient by balancing the spectral and spatial fidelity. By multiplying the optimized details and injection gain, the final HRMS is obtained with the injection model. The performance of the proposed model is analyzed and a large number of tests are carried out on various satellite datasets. Compared to some advanced pansharpening methods, the results prove that our method can achieve the best fusion quality both subjectively and objectively.

Proximal PanNet: A Model-Based Deep Network for Pansharpening

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.

A Competent Convolutional Sparse Representation Model for Pan-Sharpening of Multi-Spectral Images

Two types of images are produced by Earth observation satellites, each having complementing spatial andspectral characteristics. Pan-sharpening (PS) is based on remote sensing and image fusion approach thatproduces a high spatial resolution multi-spectral image by merging spectral information from a low spatialresolution multispectral (MS) image with intrinsic spatial details from a high spatial resolution panchromatic(PAN) image. Traditional pan-sharpening methods continue to seek for a fused image that contains thenecessary spatial and spectral information. This work proposes a pan-sharpening method based on a recentinvention, convolutional sparse representation (CSR). Geometric structural characteristics are extracted fromthe PAN image using a CSR-based filtering procedure. The challenge of learning filters, convolutional basispursuit denoising (CBPDN), is handled using a modified dictionary learning method based on the concept ofAlternating Direction Method of Multipliers (ADMM). The retrieved details are put into MS bands usingapplicable weighting coefficients. Because the proposed fusion model avoids the standard patch-basedmethod, spatial and structural features are preserved while spectral quality is maintained. The spectraldistortion index SAM and the spatial measure ERGAS improve by 4.4 and 6.2 percent, respectively, whencompared to SR-based techniques. The computational complexity is reduced by 200 seconds when compared
 to the most recent SR-based fusion technique. The proposed method's efficacy is demonstrated by reduced-scale and full-scale experimental findings utilising the QuickBird and GeoEye-1 datasets.

A Two-Stage Pansharpening Method for the Fusion of Remote-Sensing Images

The pansharpening (PS) of remote-sensing images aims to fuse a high-resolution panchromatic image with several low-resolution multispectral images for obtaining a high-resolution multispectral image. In this work, a two-stage PS model is proposed by integrating the ideas of component replacement and the variational method. The global sparse gradient of the panchromatic image is extracted by variational method, and the weight function is constructed by combining the gradient of multispectral image in which the global sparse gradient can provide more robust gradient information. Furthermore, we refine the results in order to reduce spatial and spectral distortions. Experimental results show that our method had high generalization ability for QuickBird, Gaofen-1, and WorldView-4 satellite data. Experimental results evaluated by seven metrics demonstrate that the proposed two-stage method enhanced spatial details subjective visual effects better than other state-of-the-art methods do. At the same time, in the process of quantitative evaluation, the method in this paper had high improvement compared with that other methods, and some of them can reach a maximal improvement of 60%.

PanCSC-Net: A Model-Driven Deep Unfolding Method for Pansharpening

Recently, deep learning (DL) approaches have been widely applied to the pansharpening problem, which is defined as fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image to obtain a high-resolution multispectral (HRMS) image. However, most DL-based methods handle this task by designing black-box network architectures to model the mapping relationship from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits their further performance improvements. To address this issue, we adopt the model-driven method to design an interpretable deep network structure for pansharpening. First, we present a new pansharpening model using the convolutional sparse coding (CSC), which is quite different from the current pansharpening frameworks. Second, an alternative algorithm is developed to optimize this model. This algorithm is further unfolded to a network, where each network module corresponds to a specific operation of the iterative algorithm. Therefore, the proposed network has clear physical interpretations, and all the learnable modules can be automatically learned in an end-to-end way from the given dataset. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.

Vision Transformer for Pansharpening

Pansharpening is a fundamental and hot-spot research topic in remote sensing image fusion. In recent years, self-attention-based transformer has attracted considerable attention in natural language processing (NLP) and introduced to attend to computer vision (CV) tasks. Inspired by great success of the vision transformer (ViT) in image classification, we propose an improved and advanced purely transformer-based model for pansharpening. In the proposed method, stacked multispectral (MS) and panchromatic (PAN) images are cropped into patches (i.e., tokens), and after a three-layer self-attention-based encoder, these tokens contain rich information. After upsampled and stitched, a high spatial resolution (HR) MS image is finally obtained. Instead of convolutional neural networks (CNNs) pursuing a short-distance dependency, our proposed method aims to build up a long-distance dependency, to make full use of more useful features. The experiments were conducted on an opening benchmark dataset, including IKONOS with four-band MS/PAN images and WorldView-2 MS images featured by eight bands. In addition, the experiments were performed on reduced and full-resolution datasets from both qualitative and quantitative evaluation aspects. The experimental results indicate the competitive performance of the proposed model than other pansharpening methods, including the state-of-the-art pansharpening algorithms based on CNN.

Optimization Algorithm Unfolding Deep Networks of Detail Injection Model for Pansharpening

Pansharpening aims at integrating a high-spatial-resolution panchromatic (PAN) image with a low-spatial-resolution multispectral (MS) image to generate a high-resolution MS (HRMS) image. It is a fundamental and significant task in the field of remotely sensed images. Classic and convolutional neural network (CNN)-based algorithms have been developed, over the last decades, for pansharpening based on the spatial detail injection model. However, these algorithms have difficulties in extracting sufficient details or lack interpretability. In this letter, we present an algorithm unfolding pansharpening (AUP) for this task. In the proposed AUP, a two-step optimization model is first designed based on the spatial detail decomposition model. Then, the iteration processes induced by an optimization model are mapped to several detailed convolution (dc) blocks to solve the detail injection by a trainable neural network. Finally, the desired MS details are obtained in end-to-end manners through a decoder. The superiority of the proposed AUP is demonstrated by extensive experiments on datasets acquired by two different kinds of satellites. Each module of the AUP is interpretable, and its fused results are with fewer spectral and spatial distortions.

Spatial and Spectral Anisotropic Tensor Total Variation-Driven Adaptive Pansharpening

This letter proposed a spatial and spectral anisotropic tensor total variation (SSATTV) driven adaptive pansharpening model for the fusion of low-resolution (LR) multispectral (MS) and panchromatic (Pan) images to the high-resolution (HR) MS images. Except for the local spectral consistency constraint-based fidelity term between HR and LR MS used for spectral preservation, the proposed model reformulated the adaptive linear constraint-based fidelity term between Pan and HR MS with the tensor-representation modeling, and particularly proposed a SSATTV prior term which imposed the spatial anisotropic TV prior between HR MS and Pan for spatial gradient feature preservation, and the spectral TV sparsity prior on HR MS for further spectral feature preservation. Furthermore, the proposed model was efficiently solved via the alternating direction method of multipliers (ADMM) scheme. Specifically, the experiments validated the superiority of proposed SSATTV method.

A Unified Pansharpening Model Based on Band-Adaptive Gradient and Detail Correction.

Pansharpening is used to fuse a panchromatic (PAN) image with a multispectral (MS) image to obtain a high-spatial-resolution multispectral (HRMS) image. Traditional pansharpening methods face difficulties in obtaining accurate details and have low computational efficiency. In this study, a unified pansharpening model based on the band-adaptive gradient and detail correction is proposed. First, a spectral fidelity constraint is designed by keeping each band of the HRMS image consistent with that of the MS image. Then, a band-adaptive gradient correction model is constructed by exploring the gradient relationship between a PAN image and each band of the MS image, so as to adaptively obtain an accurate spatial structure for the estimated HRMS image. To refine the spatial details, a detail correction constraint is defined based on the parameter transfer by designing a reduced-scale parameter acquisition model. Finally, a unified model is constructed based on the gradient and detail corrections, which is then solved by an alternating direction multiplier method. Both reduced-scale and full-scale experiments are conducted on several datasets. Compared with state-of-the-art pansharpening methods, the proposed method can achieve the best results in terms of fusion quality and has high efficiency. Specifically, our method improves the SAM and ERGAS metrics by 17.6% and 21.2% respectively compared to the traditional approach with the best average values, and improves these two metrics by 4.3% and 10.3% respectively compared to the learning-based approach with the best average values.

A New Context-Aware Details Injection Fidelity With Adaptive Coefficients Estimation for Variational Pansharpening

Pansharpening is related to the fusion of a low spatial resolution multispectral (MS) image retaining an abundant spectral content and a high spatial resolution panchromatic (PAN) image to obtain a product with both the abundant spectral content of the former and the high spatial resolution of the latter. Many previous studies are only focused on the global or local relationship between the PAN image and the corresponding high-resolution multispectral (HRMS) image. However, we found that the relationship between PAN and HRMS images in the gradient domain can be better explored through the image context. In this article, we propose context-aware details injection fidelity (CDIF) with adaptive coefficients estimation, which can fully explore the complicated relationship between the PAN image and the HRMS image in the gradient domain. More specifically, we apply a clustering method to divide the pixels of an image into different context-based regions. Afterward, the adaptive coefficients are estimated by using a regression-based method for each region. The CDIF is effective in extracting the main features from the two inputs to be fused. In addition, we integrate the CDIF with a conventional fidelity term and a total variation regularization to formulate a novel variational pansharpening model that is solved by designing an algorithm based on the alternating direction method of multiplier (ADMM) framework. Qualitative and quantitative assessments on different datasets support the effectiveness and robustness of the proposed method. The code is available at <uri>https://github.com/liangjiandeng/CDIF</uri>.