Low Resolution Multispectral Image Research Articles

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.

DECISION-BASED FUSION OF PANSHARPENED VHR SATELLITE IMAGES USING TWO-LEVEL ROLLING SELF-GUIDANCE FILTERING AND EDGE INFORMATION

Abstract. Pan-sharpening (PS) fuses low-resolution multispectral (LR MS) images with high-resolution panchromatic (HR PAN) bands to produce HR MS data. Current PS methods either better maintain the spectral information of MS images, or better transfer the PAN spatial details to the MS bands. In this study, we propose a decision-based fusion method that integrates two basic pan-sharpened very-high-resolution (VHR) satellite imageries taking advantage of both images simultaneously. It uses two-level rolling self-guidance filtering (RSGF) and Canny edge detection. The method is tested on Worldview (WV)-2 and WV-4 VHR satellite images on the San Fransisco and New York areas, using four PS algorithms. Results indicate that the proposed method increased the overall spectral-spatial quality of the base pan-sharpened images by 7.2% and 9.8% for the San Fransisco and New York areas, respectively. Our method therefore effectively addresses decision-level fusion of different base pan-sharpened images.

Pan-sharpening of remote sensing images based on gradient projection and cross fusion

ABSTRACT Pan-sharpening is the fusion of panchromatic (PAN) image and multispectral (MS) image through certain rules to generate high-resolution multispectral (HRMS) image with vivid spatial details and uniform spectral distribution. It has become an important technology in remote sensing image processing. Recently, convolutional neural networks based on deep learning have achieved remarkable results in the field of pan-sharpening. Guided by this method, this paper proposes a pan-sharpening network PSAM-NET for remote sensing images based on depth expansion combined with Cross-Attention Fusion. It consists of gradient projection and the main image fusion module. The gradient projection mainly generates the fusion module by stacking low-resolution multispectral images and panchromatic images alternately, and two deep prior regularized optimization problem formulas are respectively solved by the gradient projection algorithm. The other principal image fusion module realizes double branch fusion, which is mainly composed of cross attention fusion and channel attention fusion, to produce an excellent fusion effect. The simulation and real data experiments were performed on the standard datasets WV2, GF-2, and QB. The qualitative analysis and quantitative comparison with the classical pan-sharpening method proved that the spatial information of the image obtained by this method is complete. The spectral distribution is uniform, and the evaluation index also has some advantages.

Pansharpening via Frequency-Aware Fusion Network With Explicit Similarity Constraints

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion still exist in their fusion results due to the insufficiency in details learning and the frequency mismatch between MS and PAN. Therefore, the improvement of spatial details at the premise of reducing spectral distortion is still a challenge. In this paper, we propose a frequency-aware fusion network (FAFNet) together with a novel high-frequency feature similarity loss to address above mentioned problems. FAFNet is mainly composed of two kinds of blocks, where the frequency aware blocks aim to extract features in the frequency domain with the help of discrete wavelet transform (DWT) layers, and the frequency fusion blocks reconstruct and transform the features from frequency domain to spatial domain with the assistance of inverse DWT (IDWT) layers. Finally, the fusion results are obtained through a convolutional block. In order to learn the correspondence, we also propose a high-frequency feature similarity loss to constrain the HF features derived from PAN and MS branches, so that HF features of PAN can reasonably be used to supplement that of MS. Experimental results on three datasets at both reduced- and full-resolution demonstrate the superiority of the proposed method compared with several state-of-the-art pansharpening models. The codes are available at https://github.com/YinghuiXing/FAFNet.

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.

Cross-Resolution Semi-Supervised Adversarial Learning for Pansharpening

Existing deep neural network (DNN)-based methods have produced good pansharpened images. However, supervised DNN-based pansharpening methods suffer from performance degradation when fusing low spatial resolution multispectral (LR MS) and panchromatic (PAN) images at full resolution. Unsupervised DNN-based methods alleviate the issue, but their training becomes difficult owing to the absence of reference images. This paper establishes a novel semi-supervised framework to jointly learn the reconstructions of fused images from reduced- and full-resolution datasets. Specifically, we propose a cross-resolution semi-supervised adversarial learning network (CrossNet), which is composed of a supervised module and an unsupervised module. In these two modules, reduced- or full-resolution source images are disentangled as resolution-invariant components and resolution-aware components by reconstructing the fused images. Moreover, cross-resolution fused images are synthesized to enhance the disentanglement of the two kinds of components. Through the reconstruction of cross-resolution fused images, supervised and unsupervised modules are also coupled efficiently. Then, the semi-supervised framework can simultaneously make use of the supervised information in reduced-resolution datasets and mitigate the performance degradation via full-resolution datasets. Besides, adversarial learning is employed to improve the consistency between resolution-invariant components of source images at different resolutions. Finally, extensive experiments on QuickBird, GeoEye-1, WorldView-2 and WorldView-3 datasets demonstrate that the proposed CrossNet can produce state-of-the-art fusion results in terms of qualitative and quantitative evaluations. The source code is available at https://github.com/RSMagneto/CrossNet.

PanDiff: A Novel Pansharpening Method Based on Denoising Diffusion Probabilistic Model

Pansharpening is a crucial image processing technique for numerous remote sensing downstream tasks, aiming to recover high spatial resolution multispectral (HRMS) images by fusing high spatial resolution panchromatic (PAN) images and low spatial resolution multispectral (LRMS) images. Most current mainstream pansharpening fusion frameworks directly learn the mapping relationships from PAN and LRMS images to HRMS images by extracting key features. However, we propose a novel pansharpening method based on the denoising diffusion probabilistic model (DDPM) called PanDiff, which learns the data distribution of the difference maps (DM) between HRMS and interpolated MS (IMS) images from a new perspective. Specifically, PanDiff decomposes the complex fusion process of PAN and LRMS images into a multi-step Markov process, and the U-Net is employed to reconstruct each step of the process from random Gaussian noise. Notably, the PAN and LRMS images serve as the injected conditions to guide the U-Net in PanDiff, rather than being the fusion objects as in other pansharpening methods. Furthermore, we propose a modal intercalibration module (MIM) to enhance the guidance effect of the PAN and LRMS images. The experiments are conducted on a freely available benchmark dataset, including GaoFen-2, QuickBird, and WorldView-3 images. The experimental results from the fusion and generalization tests effectively demonstrate the outstanding fusion performance and high robustness of PanDiff. Fig. 1 depicts the results of the proposed method performed on various scenes. Additionally, the ablation experiments confirm the rationale behind PanDiff’s construction.

A Novel Spatial Fidelity With Learnable Nonlinear Mapping for Panchromatic Sharpening

The purpose of panchromatic sharpening, i.e., pansharpening, is to fuse a low spatial resolution multispectral (LRMS) image with a high spatial resolution panchromatic (PAN) image, aiming to obtain a high spatial resolution multispectral (HRMS) image. Pansharpening models based on variational optimization consist of a spectral fidelity term, a spatial fidelity term, and a regularization term. Most of the methods assume that the existing PAN image and the homologous HRMS image satisfy the global or local linear relationship, which could be far from the real case, thus causing sub-optimal performance. Inspired by the nonlinear mapping ability of machine learning (ML) techniques, we propose a novel spatial fidelity term with learnable nonlinear mapping (LNM-SF), which trains an implicit functional operator via a specifically designed convolutional neural network (CNN) and efficiently constructs the nonlinear relationship between the known PAN and the latent HRMS images. Relying upon the above description of the spatial fidelity term, a new variational model with a learnable nonlinear mapping in the spatial fidelity term for pansharpening, named LNM-PS, is simply integrated by the conventional spectral fidelity term into the proposed LNM-SF. To effectively solve the resulting optimization problem, we develop an alternating direction method of multipliers (ADMM)-based algorithm with the fast iterative shrinkage-thresholding algorithm (FISTA) as inner solver. Extensive numerical experiments on different datasets, assessing the performance both at reduced-resolution and full-resolution, show the superiority of the proposed LNM-PS method. The code is available at https://github.com/liangjiandeng/-LNM-PS.

Multiresolution Analysis Pansharpening Based on Variation Factor for Multispectral and Panchromatic Images From Different Times

Most pansharpening methods refer to the fusion of the original low-resolution multispectral (MS) and high-resolution panchromatic images (PAN) acquired simultaneously over the same area. Due to its good robustness, multiresolution analysis (MRA) has become one of the important categories of pansharpening methods. However, when only MS and PAN images acquired at different times can be provided, the fusion results from current MRA methods are often not ideal due to the failure to effectively analyze multitemporal misalignments between MS and PAN images from different times. To solve this issue, MRA pansharpening based on variation factor for MS and PAN images from different times is proposed. The multi-resolution analysis pansharpening based on dual-scale regression model is first established, and the variation factor is then introduced to effectively analyze the multitemporal misalignments by using alternating direction method of multipliers (ADMM), yielding the final fusion results. Experiments with synthetic and real datasets show that the proposed method exhibits significant performance improvement compared to the traditional pansharpening methods, as well as the state-of-the-art MRA methods. Visual comparisons demonstrate that the variation factor introduces encouraging improvements in the compensation of multi-temporal misalignments in ground objects and advances pansharpening applications for MS and PAN images acquired at different times.

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.

PSCF-Net: Deeply Coupled Feedback Network for Pansharpening

Pansharpening tasks are the fusion of a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image to generate a high-resolution multispectral (HRMS) image. Recently, the pansharpening method based on deep-learning (DL) has received widespread attention because of its powerful fitting ability and efficient feature extraction. Since there is currently no method to make full use of different levels of feature information of PAN images to deeply fuse with MS images, we propose a new end-to-end deeply coupled feedback network to achieve high-quality image fusion at the feature level and this network named PSCF-Net. First, features are extracted from PAN images and MS images by different feature extraction blocks. Then, these features are deeply fused through two subnetworks composed of coupled feedback blocks, which can achieve high-quality fusion of features of different levels and images through coupling and feedback mechanisms. Finally, the feature maps of the two subnetworks are output as the final HRMS image through a channel integration layer. In order to make full use of the spatial information of PAN images and the spectral information of LRMS images, the extracted features include the features of MS images and the low- and high-level features of PAN images, and the low-level features of PAN images are injected with spectral information before being input to the subnetwork. At training time, we use SmoothL1 combine with Structural Similarity as the loss function in the network, and we experiment on IKONOS and WorldView-2 datasets, respectively. The experimental results of reduced- and full-scale show that the deeply coupled feedback network we propose is superior to some of the current popular traditional methods and DL-based methods. Source code is available at https://github.com/ahu-dsp/PSCF-Net.

A framelet sparse reconstruction method for pansharpening with guaranteed convergence

Pansharpening refers to the super resolution of a low-resolution multispectral (LR-MS) image in virtue of an aligned panchromatic (PAN) image. Such an inverse problem mainly requires a proper use of the spatial information from the auxiliary PAN image. In this paper, we suggest a nonconvex regularization model for pansharpening via framelet sparse reconstruction, called NC-FSRM, which investigates the coefficient similarity among the underlying high-resolution MS (HR-MS) and PAN images on the framelet domain, then characterizes the strong statistical sparsity of their error using $ \ell_0 $ norm. Compared with previous methods, NC-FSRM can more precisely and concisely establish the relation between the underlying HR-MS and PAN images. In particular, the piece-wise smoothness prior of the former can simultaneously be captured without adding additional regularizers. For solving the suggested nonconvex model, we further develop an efficient proximal alternating minimization (PAM) based algorithm, which is theoretically proven to converge to the coordinatewise minimizers under some mild assumptions. Numerical experiments conducted on different datasets demonstrate the superiority of the suggested NC-FSRM compared with other state-of-the-art pansharpening methods. The source code is publicly available at https://github.com/zhongchengwu/code_ncFSRM.

CADUI: Cross-Attention-Based Depth Unfolding Iteration Network for Pansharpening Remote Sensing Images

Pansharpening is an important technology for remote sensing imaging systems to obtain high-resolution multispectral (HRMS) images. It mainly obtains high-resolution multi-spectral (HRMS) images with uniform spectral distribution and rich spatial details by fusing low-resolution multi-spectral (LRMS) images and high-spatial-resolution panchromatic (PAN) images. Therefore, how to extract features completely and reconstruct images with high quality is critical to obtain ideal fusion images. In this paper, we propose a new pansharpening method, called the Cross Attention-based Depth Unfolding Iteration Network for Pan-sharpening remote sensing images (CADUI), which achieves the desired fusion effect by iteratively optimizing the deep prior regularization and combining it with a cross-attention mechanism. The network consists of two parts: optimized iterations of deep prior regularization (DEIN-Block) and cross-attention mechanism (CAFM-Block). Among them, DEIN-Block introduces the depth prior as an implicit regularization and improves the adaptability and representation ability of the relevant data of the reconstructed image through iteration. CAFM-Block realizes dual-branch fusion through cross-attention fusion and channel-attention fusion to achieve better fusion results. Simulation experiments and real experiments are carried out on the standard datasets QuikBird (QB) and WorldView-2 (WV2). Through quantitative comparison and qualitative analysis, it is proved that the method is superior to the existing methods.

Deep Adaptive Pansharpening via Uncertainty-Aware Image Fusion

Pansharpening is a procedure that fuses high-resolution panchromatic (PAN) images and low-resolution multispectral (LMS) images to derive high-resolution multispectral (HMS) images. Despite its rapid development, most existing pansharpening techniques integrate the information of PAN and LMS invariantly in the spatial dimension, ignoring the uneven spatial dependence of restoring HMS with the aid of PAN information and resulting in ineffective fusion results. In this work, we propose an Uncertainty-aware Adaptive Pansharpening Network (UAPN) that integrates PAN information spatial-variantly to restore LMS information with an uncertainty mechanism. Specifically, we first estimate the epistemic and aleatoric uncertainties together, which model the spatial-variant distributions of restoring the LMS image to the HMS image. Then, we introduce Uncertainty-conditioned Adaptive Convolution (UAC) to adaptively integrate LMS and PAN information, where its parameters are spatially variable by conditioning on the uncertainty estimations. Furthermore, we propose a multi-stage uncertainty-driven loss function to explicitly force the network to concentrate on restoring challenging areas of the LMS image. Extensive experimental results demonstrate the superiority of our UAPN with fewer parameters and flops, outperforming other state-of-the-art methods both qualitatively and quantitatively on multiple satellite datasets. The code is available at https://github.com/keviner1/UAPN..

Unsupervised Pansharpening Method Using Residual Network With Spatial Texture Attention

Recently, deep learning has become one of the most popular tools for pansharpening, many relevant methods have been investigated and reflected great performance. However, a non-negligible problem is the absence of ground-truth (GT). A common solution is using degraded images as training input and the original images are employed as GT. The learned mapping between low resolution (LR) and high resolution (HR) is simulated, is not real, which may cause spectral distortion or insufficient spatial texture enhancement of fused images. In order to address the drawback, a novel unsupervised attention pansharpening net (UAP-Net) is proposed. The proposed UAP-Net mainly contains two major components: 1) the deep residual network (DRN) and 2) spatial texture attention block (STAB). The DRN aims to extract spectral features and spatial details features from low-resolution multi-spectral (LRMS) and panchromatic (PAN), and to fuse those features to make them more representative. The designed STAB adopts the high-frequency component of corresponding input PAN as the weight to enhance the spatial details of the residual block output features. Moreover, a new loss function including two spatial losses and two spectral losses are established. The losses are calculated in the spatial domain and the frequency domain, respectively. Experiments on Gaofen-2 and Worldview-2 remote sensing data demonstrate that the proposed UAP-Net could fuse PAN and LRMS images effectively without the help of high-resolution multi-spectral (HRMS). The proposed framework is fully general and can be used for many multisource remote sensing image fusion, and achieves optimal performance in terms of both the subjective visual effect and the quantitative evaluation.

Hyperspectral Multispectral Image Fusion via Fast Matrix Truncated Singular Value Decomposition

Recently, methods for obtaining a high spatial resolution hyperspectral image (HR-HSI) by fusing a low spatial resolution hyperspectral image (LR-HSI) and high spatial resolution multispectral image (HR-MSI) have become increasingly popular. However, most fusion methods require knowing the point spread function (PSF) or the spectral response function (SRF) in advance, which are uncertain and thus limit the practicability of these fusion methods. To solve this problem, we propose a fast fusion method based on the matrix truncated singular value decomposition (FTMSVD) without using the SRF, in which our first finding about the similarity between the HR-HSI and HR-MSI is utilized after matrix truncated singular value decomposition (TMSVD). We tested the FTMSVD method on two simulated data sets, Pavia University and CAVE, and a real data set wherein the remote sensing images are generated by two different spectral cameras, Sentinel 2 and Hyperion. The advantages of FTMSVD method are demonstrated by the experimental results for all data sets. Compared with the state-of-the-art non-blind methods, our proposed method can achieve more effective fusion results while reducing the fusing time to less than 1% of such methods; moreover, our proposed method can improve the PSNR value by up to 16 dB compared with the state-of-the-art blind methods.

DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing Images

Pansharpening is one of the main research topics in the field of remote sensing image processing. In pansharpening, the spectral information from a low spatial resolution multispectral (LRMS) image and the spatial information from a high spatial resolution panchromatic (PAN) image are integrated to obtain a high spatial resolution multispectral (HRMS) image. As a prerequisite for the application of LRMS and PAN images, pansharpening has received extensive attention from researchers, and many pansharpening methods based on convolutional neural networks (CNN) have been proposed. However, most CNN-based methods regard pansharpening as a super-resolution reconstruction problem, which may not make full use of the feature information in two types of source images. Inspired by the PanNet model, this paper proposes a detail injection-based two-branch network (DiTBN) for pansharpening. In order to obtain the most abundant spatial detail features, a two-branch network is designed to extract features from the high-frequency component of the PAN image and the multispectral image. Moreover, the feature information provided by source images is reused in the network to further improve information utilization. In order to avoid the training difficulty for a real dataset, a new loss function is introduced to enhance the spectral and spatial consistency between the fused HRMS image and the input images. Experiments on different datasets show that the proposed method achieves excellent performance in both qualitative and quantitative evaluations as compared with several advanced pansharpening methods.