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This letter presents a novel image quality index which extends the Universal Image Quality Index for monochrome images to multispectral and hyperspectral images through hypercomplex numbers. The proposed index is based on the computation of the hypercomplex correlation coefficient between the reference and tested images, which jointly measures spectral and spatial distortions. Experimental results, both from true and simulated images, are presented on spaceborne and airborne visible/infrared images. The results prove accurate measurements of inter- and intraband distortions even when anomalous pixel values are concentrated on few bands.

In this paper, we propose an optimum algorithm, in the minimum mean-square-error (mmse) sense, for panchromatic (Pan) sharpening of very high resolution multispectral (MS) images. The solution minimizes the squared error between the original MS image and the fusion result obtained by spatially enhancing a degraded version of the MS image through a degraded version, by the same scale factor, of the Pan image. The fusion result is also optimal at full scale under the assumption of invariance of the fusion parameters across spatial scales. The following two versions of the algorithm are presented: a local mmse (lmmse) solution and a fast implementation which globally optimizes the fusion parameters with a moderate performance loss with respect to the lmmse version. We show that the proposed method is computationally practical, even in the case of local optimization, and it outperforms the best state-of-the-art Pan-sharpening algorithms, as resulted from the IEEE Data Fusion Contest 2006, on true Ikonos and QuickBird data and on simulated Pleiades data.

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS + Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.

This work presents a novel multisensor image fusion algorithm, which extends panchrmomatic sharpening of multispectral (MS) data through intensity modulation to the integration of MS and synthetic aperture radar (SAR) imagery. The method relies on SAR texture, extracted by ratioing the despeckled SAR image to its low-pass approximation. SAR texture is used to modulate the generalized intensity (GI) of the MS image, which is given by a linear transform extending intensity-hue-saturation transform to an arbitrary number of bands. Before modulation, the GI is enhanced by injection of high-pass details extracted from the available panchrmomatic image by means of the "a/spl grave/-trous" wavelet decomposition. The texture-modulated panchrmomatic-sharpened GI replaces the GI calculated from the resampled original MS data. Then, the inverse transform is applied to obtain the fusion product. Experimental results are presented on Landsat-7 Enhanced Thematic Mapper Plus and European Remote Sensing 2 satellite images of an urban area. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes.

In this letter, we investigate the effects of pansharpening (PS) applied to multispectral (MS) multitemporal images in change-detection (CD) applications. Although CD maps computed from pansharpened data show an enhanced spatial resolution, they can suffer from errors due to artifacts induced by the fusion process. The rationale of our analysis consists in understanding to which extent such artifacts can affect spatially enhanced CD maps. To this end, a quantitative analysis is performed which is based on a novel strategy that exploits similarity measures to rank PS methods according to their impact on CD performance. Many multiresolution fusion algorithms are considered, and CD results obtained from original MS and from spatially enhanced data are compared.

We present an experimental study on the effects of pansharp-ening on target detection from multispectral and panchromatic images acquired by very-high resolution satellite sensors. Original and spatially-enhanced image data are used to detect relatively small targets (vehicles on an airport scenario) characterized by a sufficiently well-defined spectral signature in the four MS bands. The aim of the paper is to compare different pansharpening methods by evaluating the performances of target detection on true MS and panchromatic data. Both linear and kernel-based one-class classification methods are considered for the target detection process.

We present a fast pan-sharpening method, namely FWLS, which is based on unsupervised segmentation of the original multispectral (MS) data for improved parameter estimation in a weighted least square fusion scheme. The use of simple thresholding of the normalized difference vegetation index (NDVI) dramatically reduces the computation time with respect to the recently proposed WLS method which is based on accurate supervised classification through kernel support vector machines. The fusion performances of the FWLS algorithm are the same that those obtained by the WLS algorithm, and even higher in some cases, since accurate extraction of vegetated/non-vegetated areas is only needed and high-performance supervised classification is generally not required for fusion parameter estimation. Experimental results and comparisons to state-of-the-art fusion methods are reported on Ikonos and QuickBird data. Both visual and objective quality assessment of the fusion results confirm the validity of the proposed FWLS algorithm.

This paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits a Weighted Least Squares estimator to calculate injection parameters in the fusion model. For each pixel of the image a weight is calculated by a classification map. The classifier used in the experiments is a Support Vector Machine in order to obtain high accuracy on each land-cover type. Results are presented and discussed on very-high resolution images acquired by Quickbird and Ikonos satellite systems. Fusion simulations on spatially degraded data and fusion tests at full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.

This paper introduces a novel approach for evaluating the quality of pansharpened multispectral (MS) imagery without resorting to reference originals. Hence, evaluations are feasible at the highest spatial resolution of the panchromatic (PAN) sensor. Wang and Bovik’s image quality index (QI) provides a statistical similarity measurement between two monochrome images. The QI values between any couple of MS bands are calculated before and after fusion and used to define a measurement of spectral distortion. Analogously, QI values between each MS band and the PAN image are calculated before and after fusion to yield a measurement of spatial distortion. The rationale is that such QI values should be unchanged after fusion, i.e., when the spectral information is translated from the coarse scale of the MS data to the fine scale of the PAN image. Experimental results, carried out on very high-resolution Ikonos data and simulated Pleiades data, demonstrate that the results provided by the proposed approach are consistent and in trend with analysis performed on spatially degraded data. However, the proposed method requires no reference originals and is therefore usable in all practical cases.

This paper presents a solution to the problem of enhancing the spatial resolution of multispectral images with high-resolution panchromatic observations. The proposed method exploits the undecimated discrete wavelet transform, which is an octave bandpass representation achieved from a conventional discrete wavelet transform by omitting all decimators and up-sampling the wavelet filter bank, and the vector multiscale Kalman filter, which is used to model the injection process of wavelet details. Kalman modelization is exploited by spatial detail analysis at coarser scales in which multispectral and panchromatic representations are known. Results are presented and discussed on very-high resolution images acquired by Quickbird satellite systems. Fusion simulations on spatially degraded data and fusion tests at the full scale reveal that an accurate and reliable PAN-sharpening is achieved by the proposed method.

Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multi-spectral (MS) and panchromatic (Pan) images (Pan-sharpening). State-of-the-art algorithms add spatial details derived from the Pan image to the MS bands according to an injection model. The capability of the model to describe the relationship between the MS and Pan images is crucial for the quality of fusion results. Although context adaptive (CA) injection models have been proposed in the framework of MRA, their adoption in CS schemes has been scarcely investigated so far. In this work a CA injection model already tested for MRA algorithms is evaluated also for CS schemes. Qualitative and quantitative results reported for IKONOS high spatial resolution data show that CA injection models are more efficient than global ones.

In this work we investigate the use of Shannon's information theory for the goal of devising quality scores of image fusion results, that do not require reference originals. In particular, the mutual information between resampled original and fused MS bands is used to measure the spectral quality, while the mutual information between the Pan image and the fused bands yields a measure of spatial quality. The rationale is that the normalized mutual information calculated either between any couple of bands, or between each MS band and the Pan image, should be unchanged after fusion, i.e., when passing from the coarse scale of the MS data to the fine scale of the Pan image. Experimental results carried out on QuickBird and Ikonos data demonstrate that the results provided by the proposed information-theoretic method are in trend with analysis performed on spatially degraded data by means of such parameters as Walds's ERGAS, Wang and Bovik's QI, and the novel Q4 score index based on quaternions theory and recently proposed by the authors. However, the novel method requires no reference and is therefore directly applicable in all practical cases.

A novel image fusion method is presented, suitable for sharpening of multispectral (MS) images by means of a panchromatic (PAN) observation. The method is based on redundant multiresolution analysis (MRA); the MS bands expanded to the finer scale of the PAN band are sharpened by adding the spatial details from the MRA representation of the PAN data. As a direct, unconditioned injection of PAN details gives unsatisfactory results, a new injection model is proposed that provides the optimum injection by maximizing a global quality index of the fused product. To this aim, a real-valued genetic algorithm (GA) has been defined and tested on Quickbird data. The optimum GA injection is driven by an index function capable of measuring different types of possible distortions in the fused images. Fusion tests are carried out on spatially degraded data to objectively compare the proposed scheme to the most promising state-of-the-art image fusion methods, and on full-resolution image data to visually assess the performance of the proposed genetic image fusion method.

This paper presents a novel image fusion method, suitable for pan-sharpening of multispectral (MS) bands, based on multiresolution analysis (MRA). The low-resolution MS bands are sharpened by injecting highpass directional details extracted from the high-resolution panchromatic (Pan) image by means of the curvelet transform, which is a nonseparable MRA, whose basis function are directional edges with progressively increasing resolution. The advantage with respect to conventional separable MRA, either decimated or not, is twofold: directional detail coefficients matching image edges may be preliminarily softthresholded to achieve denoising better than in the separable wavelet domain; modeling of the relationships between highresolution detail coefficients of MS bands and of the Pan image is more fitting, being carried out in a directional wavelet domain. Experiments carried out on a very-high resolution MS + Pan QuickBird image show that the proposed curvelet method quantitatively outperforms state-of-the art image fusion methods, in terms of geometric, radiometric, and spectral fidelity.

This paper presents a novel multisensor image fusion algorithm, which extends pan-sharpening of multispectral (MS) data through intensity modulation to the integration of MS and SAR imagery. The method relies on SAR texture, extracted by ratioing a map of a SAR feature to its lowpass approximation. SAR texture is used to modulate the generalized intensity (GI) of the MS image, which is given by a linear transform extending Intensity-Hue-Saturation (IHS) transform to an arbitrary number of bands. Before modulation, the GI is enhanced by injection of highpass details extracted from the available Pan image by means of the "a-trous" wavelet decomposition. The texture-modulated pan-sharpened GI replaces the GI calculated from the resampled original MS data; then the inverse transform is applied to obtain the fusion product. Experimental results are presented on Landsat-7/ETM+ and ERS-2 images of an urban area. The results demonstrate accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where SAR texture information enhances the fusion product, which can be usefully applied for both visual analysis and classification purposes.

This paper addresses the modeling of wavelet coefficients for multispectral (MS) band sharpening based on undecimated multiresolution analysis (MRA). The coarse MS bands are sharpened by injecting highpass details taken from a high-resolution panchromatic (Pan) image. Besides the MRA, crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extrapolated to the finer resolution in order to weight the Pan details to be injected. The goal is that the merged MS images are most similar to what the MS sensor would collect if it had the same resolution as the broadband Pan imager. Three injection models embedded in an ''a trous'' wavelet decomposition will be described and compared on a test set of very high-resolution QuickBird MS+Pan data. Two models work on approximation and detail coefficients, respectively, and provide a certain degree of unmixing of coarse MS pixels. The third model is based on spectral fidelity of the merged image to the (resampled) original MS data, that is, no unmixing is attempted. It is much simpler than the other two because it does not require calculation of local statistics. Fusion comparisons on spatially degraded data, of which higher-resolution true MS data are available for reference, show that the former two models yield better results than the latter, in terms of both radiometric and spectral fidelity. The latter, however, yields a reliable sharpening unaffected by local artifacts, regardless of landscape complexity. When local statistics of wavelet coefficients are used, the model estimated on approximation yields slightly better but considerably stabler results than that calculated starting from bandpass details.

This paper critically reviews state-of-the-art and advanced methods for multispectral (MS) and panchromatic (Pan) image fusion based on either intensity-hue-saturation (IHS) transformation, or redundant multiresolution analysis (MRA). In either cases, lower-resolution MS bands are sharpened by injecting details taken from the higher-resolution Pan image. Crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extended to the finer resolution to weight the Pan details to be injected. Two injection models embedded in an a trous wavelet decomposition will be compared on a test set of very high resolution QuickBird MS+Pan data. One works on approximations and provides a partial unmixing of coarse MS pixels via high-resolution Pan. Another is based on spectral fidelity of original and merged MS data. Fusion comparisons on spatially degraded data, whose high-resolution MS originals are available for reference, show that the former performs better than the latter, in terms of both spatial and spectral fidelity.

The paper presents a multisensor image fusion algorithm which extends the solutions proposed for pan-sharpening of multispectral (MS) data through intensity modulation, to the integration of SAR and multispectral imagery. The algorithm is based on the computation of the ratio between a speckle-filtered SAR image and a low-pass approximation, obtained by 'a-trous' wavelet decomposition, of the same filtered SAR image. This ratio modulates the intensity of the multispectral image, which is obtained by applying a linear transformation, i.e. , a generalized IHS transform, to the original MS data. The modulated intensity image substitutes the original intensity image of the multispectral data and the inverse transform is applied to obtain the fused multispectral image. Experimental results are presented on Landsat ETM+ and ERS SAR images of an urban area. The results prove accurate spectral preservation on vegetated regions, bare soil, and also on textured areas (buildings and road network) where information from SAR enhances the fused result which can be successfully applied both for display and classification purposes.

This paper addresses multispectral (MS) band sharpening based on undecimated multiresolution analysis (MRA). The coarse MS bands are sharpened by injecting highpass details taken from a high resolution panchromatic (Pan) image. Besides the MRA, crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extrapolated to the finer resolution in order to weight the Pan details to be injected. The goal is that the merged MS images are most similar to what the MS sensor would collect if it had the same resolution as the broadband Pan imager. Three injection models embedded in an "`a trous" wavelet decomposition will be described and compared on a test set of very high resolution QuickBird MS + Pan data. Fusion comparisons on spatially degraded data, of which higher-resolution true MS data are available for reference, will be presented.

This letter focuses on quality assessment of fusion of multispectral (MS) images with high-resolution panchromatic (Pan) observations. A new quality index suitable for MS imagery having four spectral bands is defined from the theory of hypercomplex numbers, or quaternions. Both spectral and radiometric distortion measurements are encapsulated in a unique measurement, simultaneously accounting for local mean bias, changes in contrast, and loss of correlation of individual bands, together with spectral distortion. Results are presented and discussed on very high-resolution QuickBird data, through comparisons between state-of-the-art and advanced MS + Pan merge algorithms.

This paper critically reviews state-of-the-art and advanced methods for multispectral (MS) and panchromatic (Pan) image fusion based on either intensity-hue-saturation (IHS) transformation, or redundant multiresolution analysis (MRA). In either cases, lower-resolution MS bands are sharpened by injecting details taken from the higher-resolution Pan image. Crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extended to the finer resolution to weight the Pan details to be injected. Two injection models embedded in an "à trous" wavelet decomposition will be compared on a test set of very high resolution QuickBird MS+Pan data. One works on approximations and provides a partial unmixing of coarse MS pixels via high-resolution Pan. Another is based on spectral fidelity of original and merged MS data. Fusion comparisons on spatially degraded data, whose high-resolution MS originals are available for reference, show that the former performs better than the latter, in terms of both spatial and spectral fidelity.

This paper addresses the modeling of wavelet coefficients for multispectral (MS) band sharpening based on undecimated multiresolution analysis (MRA). The coarse MS bands are sharpened by injecting highpass details taken from a high resolution panchromatic (Pan) image. Besides the MRA, crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. The goal is that the merged MS images are most similar to what the MS sensor would collect if it had the same resolution as the broadband Pan imager. Three injection models embedded in an "a trous" wavelet decomposition will be described and compared on a test set of very high resolution QuickBird MS+Pan data. Two models work on approximation and detail coefficients, respectively, and provide a certain degree of unmixing of coarse MS pixels. The third model is based on spectral fidelity of the merged image to the (resampled) original MS data, that is, no unmixing is attempted. Fusion comparisons on spatially degraded data, of which higher-resolution true MS data are available for reference, show that the former two models yield better results than the latter, in terms of both radiometric and spectral fidelity. The latter, however, yields a reliable sharpening unaffected by local artifacts, regardless of landscape complexity. When local statistics of wavelet coefficients are utilized, the model estimated on approximation yields slightly better but considerably stabler results than that calculated starting from bandpass details.