High Spatial Resolution Panchromatic Image Research Articles

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.

Spatial-Spectral Dual Back-Projection Network for Pansharpening

Deep unfolding networks have obtained satisfactory performance in the pansharpening task owing to their sufficient interpretability. Inspired by the back-projection (BP) mechanism, we propose a BP-driven model, spatial-spectral dual back-project network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DBPN), to fuse the low spatial resolution multispectral (LR MS) and the high spatial resolution panchromatic (PAN) images by exploiting the BP in spatial and spectral domains. Specifically, the proposed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DBPN is made up of a spatial BP network, a spectral BP network, and a reconstruction network. In the spatial BP network, spatial down- and up-projection modules are derived from BP, which is responsible for the projection of the LR MS image into the spatial domain. By analogy with the spatial BP, we reformulate the degradation between high spatial resolution multispectral (HR MS) and PAN images as spectral down- and up-projections. Then, the spectral BP network is constructed for the projection of the PAN image along the channel dimension. Finally, the features from spatial and spectral BP networks are integrated to produce the desired HR MS image through the reconstruction network. Compared to the state-of-the-art methods, extensive experiments on QuickBird, GeoEye-1, and WorldView-2 datasets demonstrate that our S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DBPN produces better HR MS images in terms of qualitative and quantitative evaluation metrics. The code of S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DBPN is released at: https://github.com/RSMagneto/S2DBPN.

LRTCFPan: Low-Rank Tensor Completion Based Framework for Pansharpening.

Pansharpening refers to the fusion of a low spatial-resolution multispectral image with a high spatial-resolution panchromatic image. In this paper, we propose a novel low-rank tensor completion (LRTC)-based framework with some regularizers for multispectral image pansharpening, called LRTCFPan. The tensor completion technique is commonly used for image recovery, but it cannot directly perform the pansharpening or, more generally, the super-resolution problem because of the formulation gap. Different from previous variational methods, we first formulate a pioneering image super-resolution (ISR) degradation model, which equivalently removes the downsampling operator and transforms the tensor completion framework. Under such a framework, the original pansharpening problem is realized by the LRTC-based technique with some deblurring regularizers. From the perspective of regularizer, we further explore a local-similarity-based dynamic detail mapping (DDM) term to more accurately capture the spatial content of the panchromatic image. Moreover, the low-tubal-rank property of multispectral images is investigated, and the low-tubal-rank prior is introduced for better completion and global characterization. To solve the proposed LRTCFPan model, we develop an alternating direction method of multipliers (ADMM)-based algorithm. Comprehensive experiments at reduced-resolution (i.e., simulated) and full-resolution (i.e., real) data exhibit that the LRTCFPan method significantly outperforms other state-of-the-art pansharpening methods. The code is publicly available at: https://github.com/zhongchengwu/code_LRTCFPan.

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.

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.

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.

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&#x0D; 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.

Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening.

Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-based methods are hard to understand and lack interpretability due to the black-box design. In this Letter, we propose a multi-level spatial details cross-extraction and injection network (MSCIN) for HS pansharpening, which introduces the mature multi-resolution analysis (MRA) technology to the neural network. Following the general idea of MRA, the proposed MSCIN divides the pansharpening process into details extraction and details injection, in which the missing details and the injection gains are estimated by two specifically designed interpretable sub-networks. Experimental results on two widely used datasets demonstrate the superiority of the proposed method.

DOCSNet: a dual-output and cross-scale strategy for pan-sharpening

ABSTRACT Pan-sharpening aims to obtain a multi-spectral image of high resolution from inputs of a high spatial resolution panchromatic image and a low spatial resolution multi-spectral image. In recent years, pan-sharpening methods based on supervised learning have achieved superior performance over traditional methods. However, all these supervised pan-sharpening methods rest upon the assumption that performance of model trained on a coarse scale can generalize well on a finer one, which is not always the case. To address this problem, we propose a novel dual-output and cross-scale learning strategy DOCSNet for pan-sharpening. DOCSNet consists of two sub-networks, ReducedNet1 and FullNet2, which are both adapted from simple three convolutional layers and progressively cascaded. ReducedNet1 is first trained on the reduced-scale training set, its parameters are frozen, and then the whole network (fixed ReducedNet1 cascaded with FullNet2) adopts a cross-scale training strategy which involves simultaneously reduced and full resolution training samples. Each sub-network has an output terminal for reduced-scale and target-scale results, respectively. To the best of our knowledge, this is the first attempt to introduce a dual-output architecture to pan-sharpening framework. Extensive experiments on GaoFen-2 and WorldView-3 satellite images demonstrate that DOCSNet outperforms other state-of-the-art pan-sharpening methods in terms of qualitative visual effects and quantitative metrics evaluations.

Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas

Image fusion of satellite sensors can generate a high-resolution multi-spectral image from inputs of a high spatial resolution panchromatic image and a low spatial resolution multi-spectral image for feature extraction and target recognition, such as enclosure seines and floating rafts. However, there is currently no clear and definite method of image fusion for different aquaculture areas distribution extraction from high-resolution satellite images. This study uses three types of high-resolution remote sensing images, GF-1 (Gaofen-1), GF-2 (Gaofen-2), and WV-2 (WorldView-2), covering the raft and enclosure seines aquacultures in the Xiangshan Bay, China, to evaluate panchromatic and multispectral image fusion techniques to determine which is the best. This study applied PCA (principal component analysis), GS (Gram-Schmidt), and NNDiffuse (nearest neighbor diffusion) algorithms to panchromatic and multispectral images fusion of GF-1, GF-2, and WV-2. Two quantitative methods are used to evaluate the fusion effect. The first used seven statistical parameters, including gray mean value, standard deviation, information entropy, average gradient, correlation coefficient, deviation index, and spectral distortion. The second is the CQmax index. Comparing the evaluation results by these seven common statistical indicators with the results of the image fusion evaluation by index CQmax, the results prove that the CQmax index can be applied to the evaluation of image fusion effects in different aquaculture areas. For the floating raft cultured area, the conclusion is consentaneous; NNDiffuse was also optimal for GF-1 and GF-2 data, and PCA was optimal for WV-2 data. For the enclosure seines culture area, the conclusion of quantitative evaluations is not consistent and it shows that there is no definite good method that can be applied to all areas; therefore, careful evaluation and selection of the best applicable image fusion method are required according to the study area and sensor images.

Detail-Injection-Model-Inspired Deep Fusion Network for Pansharpening

Pansharpening is an image fusion procedure, which aims to produce a high spatial resolution multispectral image by combining a low spatial resolution multispectral image and a high spatial resolution panchromatic image. The most popular and successful paradigm for pansharpening is the framework known as detail injection, while it cannot fully exploit complex and non-linear complementary features of both images. In this paper, we propose a detail injection model inspired deep fusion network for pansharpening (DIM-FuNet). Firstly, by treating pansharpening as a complicated and non-linear details learning and injection problem, we establish a unified optimizing detail-injection model with triple detail fidelity terms: 1) a band-dependent spatial detail fidelity term, 2) a local detail fidelity term and 3) a complicated details synthesis term. Secondly, the model is optimized via the iterative gradient descent and unfolded into a deep convolutional neural network. Subsequently, the unrolling network has triple branches, in which, a point-wise convolutional sub-network, a depth-wise convolutional sub-network are corresponding to the former two detail constrained terms, and an adaptive weighted reconstruction module with a fusion sub-network to aggregate details of two branches and synthesis the final complicated details. Finally, the deep unrolling network is trained in end-to-end manners. Different from traditional deep fusion networks, the architecture design of DIM-FuNet is guided by the optimizing model and thus promotes better interpretability. Experimental results on reduced and full-resolution demonstrate the effectiveness of the proposed DIM-FuNet which achieves the best performance compared with the state-of-the-art pansharpening method.

No Reference Pansharpened Image Quality Assessment Through Deep Feature Similarity

Pansharpening refers to the process of enhancing the spatial resolution of a multispectral image with the help of a high spatial resolution panchromatic image. Quality assessment (QA) of pansharpened images helps provide a formal framework for the analysis and design of pansharpening methods and is thus extremely important. However, lack of availability of a reference multispectral image makes QA of pansharpening algorithms a challenging task. Given the popular use of QA algorithms that use a reference, this paper focuses on predicting the quality under a &#x2018;no-reference&#x0027; (NR) setting. Specifically, a learning based NR pansharpened image quality assessment (IQA) approach is adopted to predict state of the art reference based measures such as <inline-formula><tex-math notation="LaTeX">$Q2^{n}$</tex-math></inline-formula> and SAM without the need of a reference. We design an end-to-end deep pansharpening IQA network to compute the similarity of deep features fused from the panchromatic and input low resolution multispectral with similar features extracted from the given pansharpened image. To train and test our learning based approach, we create a large corpus of pansharpened images belonging to different satellites and thematic scenes by applying different pansharpening algorithms. Our experiments demonstrate that our NR pansharpened IQA algorithm achieves excellent performance and generalizes well across different satellites and resolutions. The source code and the dataset will be made available at <uri>https://github.com/neerajbadal/DPIQA</uri>.

Multiresolution Analysis Based on Dual-Scale Regression for Pansharpening

Pansharpening technique is used to merge the original multispectral image (MS) with a high spatial resolution panchromatic image (PAN). Due to its robustness, the multiresolution analysis (MRA) is an important part of pansharpening. The scale regression model is effective for improving MRA. However, the existing MRA based on scale regression results into single-scale regression information, thus affecting the final pansharpening result. To address this problem, in this work, we propose a dual-scale regression-based MRA for pansharpening. First, we establish a scale regression-based model. Then, this model is improved using a high-pass modulation (HPM) injection scheme. Finally, the dual-scale information is added to the scale regression to construct the dual-scale regression for obtaining the final pansharpening result. We perform experiments using five datasets. The results show that the proposed method obtains a better pansharpening result as compared to various state-of-the-art MRA methods. In addition, the quantitative and qualitative analysis of the results shows that the proposed method achieves appropriate spatial and spectral resolution fusion. Therefore, it has a great potential in pansharpening technique.

A Dual-Branch Detail Extraction Network for Hyperspectral Pansharpening

Hyperspectral (HS) pansharpening aims at creating a high-resolution hyperspectral (HR-HS) image by integrating a high spatial resolution panchromatic (HR-PAN) image with a low-resolution hyperspectral (LR-HS) image. It is an important preprocessing procedure in many remote sensing tasks. Most of the existing pansharpening methods train a specific convolutional neural network (CNN) model for each type of dataset with the same number of spectral bands. The main contribution of this study is to propose a new dual-branch detail extraction pansharpening network (called DBDENet) that can sharpen HS images with any number of spectral bands using a single pre-trained model by fine-tuning the parameters of a small module in the network. Specifically, DBDENet extracts spatial details from LR-HS and HR-PAN images by two bidirectional branches of the dual-branch detail extraction network level by level. For each level, the spatial details captured from the HR-PAN and those of the LR-HS images are fused by a spatial cross attention fusion module (SCAFM). The spatial details fused by the last SCAFM module are injected into the upsampled HS image to obtain an HR-HS image. Experimental results prove to show the proposed DBDENet is superior to other widely accepted state-of-the-art methods in terms of objective indicators and visual appearance.

Integrated Fusion for Panchromatic, Multispectral, Hyperspectral Remote Sensing Images With Different Swath Widths

Zi Yuan (ZY)-1 02D satellite simultaneously provides the low spatial resolution (LR) and narrow swath-width hyperspectral (HS) image, the moderate spatial resolution (MR) multispectral (MS) image with a wider swath width, and the high spatial resolution (HR) panchromatic (PAN) image with the same wide swath width to the MR MS. How to comprehensively integrate their complementary advantages to obtain the wide swath-width and high-fidelity HR HS image is interesting but challenging. In this paper, we propose an integrated fusion method for the HR PAN, MR MS, and LR HS images with different swath widths, to generate the optimal wide swath-width HR HS image. The proposed method is based on the encoder-decoder learning framework. In the proposed fusion framework, a novel multi-branch encoder structure with an enhanced HS-encoder module and the multilevel spatial-spectral aggregation block is designed, by considering the difference in the spatial and spectral resolution among the multi-sensor images. The experiments on synthetic and real datasets from both qualitative and quantitative aspects demonstrated the competitive performance of the proposed method.

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>.

Fast and High-Quality Blind Multi-Spectral Image Pansharpening

Blind pansharpening addresses the problem of generating a high spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution multi-spectral (LRMS) image with the guidance of its associated spatially misaligned high spatial-resolution panchromatic (PAN) image without parametric side information. In this paper, we propose a fast approach to blind pansharpening and achieve state-of-the-art image reconstruction quality. Typical blind pansharpening algorithms are often computationally intensive since the blur kernel and the target HRMS image are often computed using iterative solvers and in an alternating fashion. To achieve fast blind pansharpening, we decouple the solution of the blur kernel and of the HRMS image. First, we estimate the blur kernel by computing the kernel coefficients with minimum total generalized variation that blur a downsampled version of the PAN image to approximate a linear combination of the LRMS image channels. Then, we estimate each channel of the HRMS image using local Laplacian prior to regularize the relationship between each HRMS channel and the PAN image. Solving the HRMS image is accelerated by both parallelizing across the channels and by fast numerical algorithms for each channel. Due to the fast scheme and the powerful priors we used on the blur kernel coefficients (total generalized variation) and on the cross-channel relationship (local Laplacian prior), numerical experiments demonstrate that our algorithm outperforms state-of-the-art model-based counterparts in terms of both computational time and reconstruction quality of the HRMS images.

A region based remote sensing image fusion using anisotropic diffusion process

ABSTRACT The aim of remote sensing image fusion is to merge the high spectral resolution multispectral (MS) image with high spatial resolution panchromatic (PAN) image to get a high spatial resolution MS image with less spectral distortion. The conventional pixel level fusion techniques suffer from the halo effect and gradient reversal. To solve this problem, a new region-based method using anisotropic diffusion (AD) for remote sensing image fusion is investigated. The basic idea is to fuse the ‘Y’ component only (of YCbCr colour space) of the MS image with the PAN image. The base layers and detail layers of the input images obtained using the AD process are segmented using the fuzzy c-means (FCM) algorithm and combined based on their spatial frequency. The fusion experiment uses three data sets. The contributions of this paper are as follows: i) it solves the chromaticity loss problem at the time of fusion, ii) the AD filter with the region-based fusion approach is brought into the context of remote sensing application for the first time, and iii) the edge info in the input images is retained. A qualitative and quantitative comparison is made with classic and recent state-of-the-art methods. The experimental results reveal that the proposed method produces promising fusion results.

QUALITY ASSESSMENT OF PANSHARPENED RASAT AND LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

Abstract. Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.