Relative Dimensionless Global Error Research Articles

ReLAP-Net: Residual Learning and Attention Based Parallel Network for Hyperspectral and Multispectral Image Fusion

Remote sensing applications require high-resolution images to obtain precise information about the Earth???s surface. Multispectral images have high spatial resolution but low spectral resolution. Hyperspectral images have high spectral resolution but low spatial resolution. This study proposes a residual learning and attention-based parallel network based on residual network and channel attention. The network performs image fusion of a high spatial resolution multispectral image and a low spatial resolution hyperspectral image. The network training and fusion experiments are conducted on four public benchmark data sets to show the effectiveness of the proposed model. The fusion performance is compared with classical signal processing???based image fusion techniques. Four image metrics are used for the quantitative evaluation of the fused images. The proposed network improved fusion ability by reducing the root mean square error and relative dimensionless global error in synthesis and increased the peak signal-to-noise ratio when compared to other state-of-the-art models.

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points.

Quality assessment of fusing Sentinel-2 and WorldView-4 imagery on Sentinel-2 spectral band values: a case study of Zagreb, Croatia

ABSTRACT Image fusion methods aim at fusing low resolution and high-resolution image to obtain a new image that provides new information for the specific application. The main goal of this article is multitemporal Sentinel-2 image fusion using single WorldView-4 satellite image for urban area monitoring. Fusing those images should provide Sentinel-2 image with similar radiometric band value as original Sentinel-2 image, but with a spatial resolution of WorldView-4. Ehlers, Brovey Transform, Modified Intensity-Hue-Saturation, High-Pass Filtering, Hyperspherical Colour Space and Wavelet resolution merge fusion techniques were used for spatial enhancement of Sentinel-2 images. Original and fused images were first compared using standard statistical parameters, mean, median and standard deviation. Image quality analysis was conducted with different objective image quality measures like root mean square error, peak signal to noise ratio, universal image quality index, structural similarity index, relative dimensionless global error, spatial correlation coefficient, relative average spectral error, spectral angle mapper, multi-scale structural similarity index. Using these quality measures helped in determining the spectral and spatial preservation of fused images. Hyperspherical colour space method was selected as the best method for image fusion of Sentinel-2 and WorldView-4 image-based on standard statistical parameters and quality measures.

A two-dimensional discrete fractional Fourier transform-based pansharpening scheme

ABSTRACTIn this paper, a new approach for fusion of multi-spectral (MS) and panchromatic (Pan) images based on 2D-discrete fractional Fourier transform (2D-DFRFT) is proposed. The proposed technique is closer in approach to the other filtering-based pansharpening schemes existing in the literature. In the proposed method histogram equalized Pan image is transformed using the 2D-DFRFT and further used to generate the pansharpened image using appropriate pansharpening rule. The angle parameters associated with the 2D-DFRFT provide additional degrees of freedom which are optimized by single-objective particle swarm optimization (PSO) algorithm for finding better pansharpening results. Simulation results of the proposed technique carried out in MATLAB are presented for IKONOS and GeoEye-1 satellite images and compared with existing fusion methods in terms of both visual perception and objective metrics such as Q-index (Q4), Spectral Angle Mapper (SAM), relative dimensionless global error (ERGAS) and quality with-no reference (QNR). It is observed that the proposed pansharpening scheme provides improved spectral and spatial quality as compared with the existing schemes. The effects of aliasing and mis-registration errors on the proposed method are also investigated and compared with existing pansharpening methods. It is seen that the proposed method is robust against aliasing and mis-registration errors.

A MAP-Based Approach for Hyperspectral Imagery Super-resolution.

In this study, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction (SRR) problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov Random Field (MRF) based energy minimization approach is proposed and proved that the solution is quadratic. The proposed approach consists of five main steps. First, the number of endmembers in the scene is determined using virtual dimensionality. Second, the endmembers and their low resolution abundance maps are computed using simplex identification via the splitted augmented Lagrangian (SISAL) and fully constrained least squares (FCLS) algorithms. Third, high resolution (HR) abundance maps are obtained using our proposed maximum a posteriori (MAP) based energy function. This energy function is minimized subject to smoothness, unity and boundary constraints. Fourth, the HR abundance maps are further enhanced with texture preserving methods. Finally, HR HSI is reconstructed using the extracted endmembers and the enhanced abundance maps. The proposed method is tested on three real HSI datasets; namely the Cave, Harvard and Hyperspectral Remote Sensing Scenes (HRSS) and compared to state-of-the-art alternative methods using peak signal to noise ratio, structural similarity, spectral angle mapper and relative dimensionless global error in synthesis metrics. It is shown that the proposed method outperforms the state of the art methods in terms of quality while preserving the spectral consistency.

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Comparison of various pan-sharpening methods using Quickbird-2 and Landsat-8 imagery

Pan-sharpening is the process of transferring the spatial resolution of panchromatic (PAN) image to a multispectral (MS) image for producing a single image with high spatial detail and rich spectral information. In this study, PAN and MS imagery of Quickbird-2 and Landsat-8 are fused separately, using ten different pan-sharpening methods such as principal component analysis (PCA), modified-intensity hue saturation (M-IHS), multiplicative, brovey transform (BT), wavelet-principal component analysis (W-PCA), hyperspectral color space (HCS), high-pass filter (HPF), Gram-Schmidt (GS), Fuze Go, and non-subsampled contourlet transform (NSCT). The effectiveness of these techniques is assessed and compared by qualitative analysis and 14 quantitative analysis methods including bias, correlation coefficient (CC), difference in variance (DIV), relative dimensionless global error in synthesis (ERGAS), universal image quality index (Q), relative average spectral error (RASE), root mean square error (RMSE), structural similarity index method (SSIM), signal-to-noise ratio (SNR), peak SNR (PSNR), spatial correlation coefficient (SCC), image entropy (E), and gradient and quality with no reference image (QNR). The results of both analysis types show that the Fuze Go and NSCT produced the best fused image with high spatial detail and rich spectral information followed by the HPF and GS.

Evaluation of pan-sharpening methods for spatial and spectral quality

Many pan-sharpening methods have been proposed to fuse the high spectral and low spatial resolution of multispectral (MS) image with the high spatial resolution of panchromatic (PAN) image to produce a multispectral image with improved spatial resolution. In this study, the effectiveness of pan-sharpening methods such as principal component analysis (PCA), brovey transform (BT), modified intensity hue saturation (M-IHS), multiplicative, wavelet-intensity-hue-saturation (W-IHS), wavelet principal component analysis (W-PCA), hyperspectral colour space (HCS), high-pass filter (HPF), gram-schmidt (GS), subtractive resolution merge (SRM), Fuze Go and Ehlers was assessed and compared by fusing the PAN and MS imagery of Quickbird-2. The qualities of the pan-sharpening methods were evaluated by both visual and quantitative analyses with respect to spatial and spectral fidelity. In quantitative analysis, the spectral indices such as spectral angle mapper (SAM), relative dimensionless global error in synthesis (ERGAS), structural similarity index method (SSIM), relative average spectral error (RASE), correlation coefficient (CC) and universal image quality index (Q) were used. The spatial indices such as spatial correlation coefficient (SCC), gradient and image entropy (E) were used. The result of both analyses revealed that the Ehlers and Fuze Go methods performed better than the other methods. The Ehlers method was superior by retaining the colour information, and Fuze Go best enhanced the spatial details in the fused image.

SU-E-J-98: Assessment of Deformable Image Registration Algorithms by Using Different Similarity Measures for the Registration Evaluation

Purpose: To evaluate deformable image registration algorithms used for 4DCT data sets registration. Furthermore, to validate several similarity measures applied on clinical 4DCT images as part of the evaluation. Methods: The capabilities of the registration algorithms — Deformable Multi Pass (DMP), Extended Deformable Multi Pass (EXDMP), structure guided deformable (SGD), and rigid — applied in the commercial software Velocity (Medical Solutions, Atlanta, GA) were evaluated by several different similarity measures applied on 4DCT images. Measures included the cross correlation (CC), root mean squared error (RMSE), relative dimensionless global error in synthesis (Erreur Relative Globale Adimensionelle de Synthese — ERGAS), image quality index (Q), structural similarity index (SSIM), feature similarity index (FSIM), gradient magnitude similarity deviation (GMSD), mutual information (MI), dice similarity coefficient (DSC), Tanimoto coefficient (TC), and specificity (S). Results: The analysis showed that for two of the registration methods (DMP and SGD), in 3 out of the 4 cases, the same registration results (the two deformed data sets were exactly the same) were obrained regardless of the phase that was used for registration. For the four cases done so far, only two similarity measures (RMSE, ERGAS) ranked rigid registration in the last place compared to the other methods. Another measure that indicated an inferior performance for the rigid registration was the MI, which ranked it in the 3rd place in front of EXDMP in only one of the cases studied. The EXDMP was ranked as the best performing algorithm by the different measures combined in 43%, DMP in 22%, SGD in 20% and RIGID in 15% of the cases considered. Conclusion: Root mean squared error, relative dimensionless global error in synthesis and mutual information appear to have an advantage over other similarity measures that are considered in the evaluation of the accuracy of deformable image registration.

Spatial and Temporal Image Fusion via Regularized Spatial Unmixing

A novel spatial and temporal data fusion model based on regularized spatial unmixing was developed to generate Landsat-like synthetic data with the fine spatial resolution of Landsat Enhanced Thematic Mapper Plus (Landsat ETM $+$ ) data and the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The proposed approach is based on the conventional spatial unmixing technique, but modified to include prior class spectra, which are estimated from pairs of MODIS and Landsat data using the spatial and temporal adaptive reflectance data fusion model. The method requires the optimization of the following three parameters: the number of classes of Landsat data, the neighborhood size of the MODIS data for spatial unmixing, and a regularization parameter added to the cost function to reduce unmixing error. Indexes of relative dimensionless global error in synthesis (ERGAS) were used to determine the best combination of the three parameters by evaluating the quality of the fused result at both Landsat and MODIS spatial resolutions. The experimental results with observed satellite data showed that the proposed approach performs better than conventional unmixing-based fusion approaches with the same parameters.

Fusion of MS and PAN Images Preserving Spectral Quality

Image fusion aims at improving spectral information in a fused image as well as adding spatial details to it. Among the existing fusion algorithms, filter-based fusion methods are the most frequently discussed cases in recent publications due to their ability to improve spatial and spectral information of multispectral (MS) and panchromatic (PAN) images. Filter-based approaches extract spatial information from the PAN image and inject it into MS images. Designing an optimal filter that is able to extract relevant and nonredundant information from the PAN image is presented in this letter. The optimal filter coefficients extracted from statistical properties of the images are more consistent with type and texture of the remotely sensed images compared with other kernels such as wavelets. Visual and statistical assessments show that the proposed algorithm clearly improves the fusion quality in terms of correlation coefficient, relative dimensionless global error in synthesis, spectral angle mapper, universal image quality index, and quality without reference, as compared with fusion methods, including improved intensity-hue-saturation, multiscale Kalman filter, Bayesian, improved nonsubsampled contourlet transform, and sparse fusion of image.

An Improved Unmixing-Based Fusion Method: Potential Application to Remote Monitoring of Inland Waters

Although remote sensing technology has been widely used to monitor inland water bodies; the lack of suitable data with high spatial and spectral resolution has severely obstructed its practical development. The objective of this study is to improve the unmixing-based fusion (UBF) method to produce fused images that maintain both spectral and spatial information from the original images. Images from Environmental Satellite 1 (HJ1) and Medium Resolution Imaging Spectrometer (MERIS) were used in this study to validate the method. An improved UBF (IUBF) algorithm is established by selecting a proper HJ1-CCD image band for each MERIS band and thereafter applying an unsupervised classification method in each sliding window. Viewing in the visual sense—the radiance and the spectrum—the results show that the improved method effectively yields images with the spatial resolution of the HJ1-CCD image and the spectrum resolution of the MERIS image. When validated using two datasets; the ERGAS index (Relative Dimensionless Global Error) indicates that IUBF is more robust than UBF. Finally, the fused data were applied to evaluate the chlorophyll a concentrations (Cchla) in Taihu Lake. The result shows that the Cchla map obtained by IUBF fusion captures more detailed information than that of MERIS.

A regularized model-based optimization framework for pan-sharpening.

Pan-sharpening is a common postprocessing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening is formulated as the problem of jointly estimating the high-resolution (HR) multispectral images to minimize an objective function comprised of the sum of squared residual errors in physically motivated observation models of the low-resolution (LR) multispectral and the HR panchromatic images and a correlation-dependent regularization term. The objective function differs from and improves upon previously reported model-based optimization approaches to pan-sharpening in two major aspects: 1) a new regularization term is introduced and 2) a highpass filter, complementary to the lowpass filter for the LR spectral observations, is introduced for the residual error corresponding to the panchromatic observation model. To obtain pan-sharpened images, an iterative algorithm is developed to solve the proposed joint minimization. The proposed algorithm is compared with previously proposed methods both visually and using established quantitative measures of SNR, spectral angle mapper, relative dimensionless global error in synthesis, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods. A software implementation is provided.

Two-dimensional fast Haar wavelet transform for satellite-image fusion

This work presents an implementation of the fast Haar wavelet transform (FHWT) mathematical algorithm assisted by the Mallat algorithm together with the ARSIS concept so as to successfully attain satellite image merging with different resolutions. Four pairs of images, namely (multispectral—pancromatic): IKONOS, GeoEye, OrbView-2, and Landsat ETM+, representing different environments are used to assess the present implementation of FHWT. In order to compare the performance of FHWT with other orthogonal and bi-orthogonal wavelets, the same satellite images are merged using other five wavelets from some of the MATLAB’s available sets, namely bior6.8, rbio6.8, db7, dmey, and Haar. After applying the six wavelets to the four regions under study, four indices are used to assess spatial and spectral quality of the merged images, namely correlation coefficient, relative average spectral error, relative dimensionless global error in synthesis, and the universal quality index Q. Moreover, shape recognition capacity is also assessed based on the resulting merged images. To do so, various objects are binarized in each of the images. Binarized versions of the objects are compared to the same objects obtained from their corresponding panchromatic image. These binarized versions are also assessed using kappa coefficient and overall accuracy. For each of the indices, the best results are obtained with the proposed method FHWT.

Spectral information analysis of image fusion data for remote sensing applications

Fusion of images with different spatial resolutions has the capability of improving visualization and spatial resolution and enhancing structural/textural information of the involved images, while preserving the spectral information in multi-spectral (MS) images. In this paper, various fusion methods have been examined in the data fusion of GeoEye-1 and QuickBird imagery, followed by subsequent image control. The effectiveness of five techniques, the Gram-Schmidt (GS), high-pass filtering (HPF), modified intensity-hue-saturation (M-IHS), fast Fourier transform (FFT)-enhanced IHS (FFT-E) and wavelet principal component analysis (W-PCA), has been evaluated through visual inspection, histogram analysis and correlation analysis. Also, image quality information is assessed by means of global spectral information (relative dimensionless global error, relative average spectral error), spectral distortion (peak signal-to-noise ratio), spectral (bias, root means square error) and spatial information (mean, standard deviation) of the fused images. In addition, the extraction of object boundary is tested and evaluated using Canny edge detection. The results show that most of the image fusion techniques preserve spectral information of original image, but occasionally with some spectral distortion. It has been found that the GS method, followed by HPF, yields the best information quality in the fused image, suitable for improving visual interpretation and data quality from the viewpoint of remote sensing applications.

Remote Sensing of Ecological Hotspots: Producing Value-added Information from Multiple Data Sources

Fusing high-spatial resolution panchromatic and high-spectral resolution multispectral images with complementary characteristics provides basis for complex land-use and land-cover type classifications. In this research, we investigated how well different pan sharpening algorithms perform when applied to single-sensor single-date and multi-senor multi–date images that encompass the Horton Plains national park (HPNP), a highly fragile eco-region that has been experiencing severe canopy depletion since 1970s, in Sri Lanka. Our aim was to deliver resolution-enhanced multitemporal images from multiple earth observation (EO) data sources in support of long-term dieback monitoring in the HPNP. We selected six candidate fusion algorithms: Brovey transform, Ehlers fusion algorithm, high-pass filter (HPF) fusion algorithm, modified intensity-hue-saturation (MIHS) fusion algorithm, principal component analysis (PCA) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to eight different aerial and satellite images taken over the HPNP during last five decades. Fused images were assessed for spectral and spatial fidelity using fifteen quantitative quality indicators and visual inspection methods. Spectral quality metrics include correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak signal-to-noise ratio index, entropy, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence, high-pass correlation coefficient, RMSE of Sobel-filtered edge images, and Fast Fourier Transform correlation. The Wavelet-PCA algorithm exhibited the worst spatial improvement while the Ehlers.MIHS and PCA fusion algorithms showed mediocre results. With respect to our multidimensional quality assessment,the HPF emerged as the best performing algorithm for single-sensor single-date and multi-sensor multi-date data fusion.We further examined the effect of fusion in the object-based image analysis framework. Our subjective analysis showed the improvement of image object candidates when panchromatic images’ high-frequency information is injected to low resolution multispectral images.

A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index

In this letter, we propose a novel method for unsupervised change detection (CD) in multitemporal Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) satellite images by using the relative dimensionless global error in synthesis index locally. In order to obtain the change image, the index is calculated around a pixel neighborhood (3 $\times$ 3 window) processing simultaneously all the spectral bands available. With the objective of finding the binary change masks, six thresholding methods are selected. A comparison between the proposed method and the change vector analysis method is reported. The accuracy CD showed in the experimental results demonstrates the effectiveness of the proposed method.

Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows

In order to satisfy the humanitarian information demand in ongoing- and post-crisis situations, earth observation (EO) data must be streamed through time-critical workflows. Data fusion serves as an integral segment of EO-based rapid-mapping workflows. Fused images form the basis for manual, semi-, and fully-automated classification steps in the information retrieval chain. Many fusion algorithms have been developed and tested for different remote sensing applications, however, the efficacy of data fusion is weakly assessed in the context of rapid-mapping workflows. In this research, we investigated how different fusion algorithms perform when applied to very high spatial resolution (VHSR) satellite images that encompass ongoing- and post-crises scenes. The evaluation entailed twelve fusion algorithms: Brovey transform, color normalization spectral sharpening (CN) algorithm, Ehlers fusion algorithm, Gram-Schmidt fusion algorithm, high-pass filter (HPF) fusion algorithm, local mean matching algorithm, local mean variance matching (LMVM) algorithm, modified intensity-hue-saturation (HIS) fusion algorithm, principal component analysis (PCA) fusion algorithm, subtractive resolution merge (SRM) fusion algorithm, the University of New Brunswick (UNB) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to GeoEye-1 satellite images taken over three geographical settings representing natural and anthropogenic crises that occurred recently: earthquake-damaged sites in Haiti, flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka. Fused images were assessed for spectral and spatial fidelity using a variety of quantitative quality indicators and visual inspection methods. Spectral quality metrics include correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak-signal-to-noise ratio index, entropy, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence, high-pass correlation coefficient, and RMSE of Sobel-filtered edge images. Under each metric, fusion algorithms were ranked and best competitors were identified. Ehlers, WV, and HPF had the best scores for the majority of spectral quality indices. UNB and Gram-Schmidt algorithms had the best scores for spatial metrics. HPF emerged as the overall best performing fusion algorithm.

Remote Sensing Image Fusion Based on Integer Wavelet Transformation and Ordered Nonnegative Independent Component Analysis

A novel remote sensing image fusion method is proposed based on integer wavelet transformation and ordered nonnegative independent component analysis (IWT-ONICA) to take advantage of the IWT characteristics of multi-resolution and high-speed computation and the fact that independent component analysis can remove higher-order redundancy. The proposed method is applied to the fusion of IKONOS satellite images. Statistical and visual results show that IWT-ONICA is superior to other fusion algorithms in terms of clarity and visual effect, while evaluation indicators such as the peak signal-to-noise ratio, correlation coefficient, spectral angle mapper, and relative dimensionless global error confirm its superior performance.

Remote Sensing Image Fusion Using Multiscale Mapped LS-SVM

The panchromatic (Pan) sharpening of multispectral (MS) bands is an important technique in the various applications of satellite remote sensing. This paper presents an MS Pan- sharpening method using the proposed multiscale mapped least-squares support vector machine (LS-SVM). Under the LS-SVM framework, the salient features underlying the image are represented by support values, and the support value transform (SVT) is developed for image information extraction. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The support value analysis is implemented by using a series of multiscale support value filters that are deduced from the mapped LS-SVM with multiscale Gaussian radial basis function kernels. Experiments are carried out on very high resolution QuickBird MS + Pan data. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed MS Pan-sharpening method performs comparable to the state-of-the-art in terms of the pertained quantitative quality evaluation indexes, such as the Spectral Angle Mapper, relative dimensionless global error in synthesis (ERGAS), modulation-transfer-function-based tool and quality index (Q4), etc. The SVT is an effective tool for remote sensing image fusion.