A survey of classical methods and new trends in pansharpening of multispectral images <s> Spectral quality assessment <s> We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematically defined and no human visual system model is explicitly employed, our experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error. Demonstrative images and an efficient MATLAB implementation of the algorithm are available online at http://anchovy.ece.utexas.edu//spl sim/zwang/research/quality_index/demo.html. <s> BIB001 </s> A survey of classical methods and new trends in pansharpening of multispectral images <s> Spectral quality assessment <s> Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/. <s> BIB002 </s> A survey of classical methods and new trends in pansharpening of multispectral images <s> Spectral quality assessment <s> 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. <s> BIB003 </s> A survey of classical methods and new trends in pansharpening of multispectral images <s> Spectral quality assessment <s> Usual image fusion methods inject features from a high spatial resolution panchromatic sensor into every low spatial resolution multispectral band trying to preserve spectral signatures and improve spatial resolution to that of the panchromatic sensor. The objective is to obtain the image that would be observed by a sensor with the same spectral response (i.e., spectral sensitivity and quantum efficiency) as the multispectral sensors and the spatial resolution of the panchromatic sensor. But in these methods, features from electromagnetic spectrum regions not covered by multispectral sensors are injected into them, and physical spectral responses of the sensors are not considered during this process. This produces some undesirable effects, such as resolution overinjection images and slightly modified spectral signatures in some features. The authors present a technique which takes into account the physical electromagnetic spectrum responses of sensors during the fusion process, which produces images closer to the image obtained by the ideal sensor than those obtained by usual wavelet-based image fusion methods. This technique is used to define a new wavelet-based image fusion method. <s> BIB004 </s> A survey of classical methods and new trends in pansharpening of multispectral images <s> Spectral quality assessment <s> Pansharpening is a pixel-level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high-resolution panchromatic image, while preserving the spectral information in the multispectral image. Various pansharpening algorithms are available in the literature, and some have been incorporated in commercial remote sensing software packages such as ERDAS Imagine® and ENVI®. The demand for high spatial and spectral resolutions imagery in applications like change analysis, environmental monitoring, cartography, and geology is increasing rapidly. Pansharpening is used extensively to generate images with high spatial and spectral resolution. The suitability of these images for various applications depends on the spectral and spatial quality of the pansharpened images. Hence, the evaluation of the spectral and spatial quality of the pansharpened images using objective quality metrics is a necessity. In this work, quantitative metrics for evaluating the quality of pansharpened images are presented. A performance comparison, using the intensity-hue-saturation (IHS)-based sharpening, Brovey sharpening, principal component analysis (PCA)-based sharpening, and a wavelet-based sharpening method, is made to better quantify their accuracies. <s> BIB005
To measure the spectral distortion due to the pansharpening process, each merged image is compared to the reference MS image, using one or more of the following quantitative indicators: (1) Spectral Angle Mapper (SAM): SAM denotes the absolute value of the angle between two vectors, whose elements are the values of the pixels for the different bands of the HRMS image and the MS image at each image location. A SAM value equal to zero denotes the absence of spectral distortion, but radiometric distortion may be present (the two pixel vectors are parallel but have different lengths). SAM is measured either in degrees or in radians and is usually averaged over the whole image to yield a global measurement of spectral distortion  . (2) Relative-shift mean (RM): The RM BIB005 of each band of the fused image helps to visualize the change in the histogram of fused image and is defined in BIB005 as the percentage of variation between the mean of the reference image and the pansharpened image. (3) Correlation coefficient (CC): The CC between each band of the reference and the pansharpened image indicates the spectral integrity of pansharpened image  . However, CC is insensitive to a constant gain and bias between two images and does not allow for subtle discrimination of possible pansharpening artifacts  . CC should be as close to 1 as possible. (4) Root mean square error (RMSE): The RMSE between each band of the reference and the pansharpened image measures the changes in radiance of the pixel values  . RMSE is a very good indicator of the spectral quality when it considered along homogeneous regions in the image BIB005 . RMSE should be as close to 0 as possible. (5) Structure Similarity Index (SSIM): SSIM BIB002 is a perceptual measure that combines several factors related to the way humans perceive the quality of the images. Beside luminosity and contrast distortions, structure distortion is considered in SSIM index and calculated locally in 8 × 8 square windows. The value varies between -1 and 1. Values close to 1 show the highest correspondence with the original images. The Universal Image Quality Index (UIQI) proposed in BIB001 can be considered a special case for SSIM index While these parameters only evaluate the difference in spectral information between each band of the merged and the reference image, in order to estimate the global spectral quality of the merged images, the following parameters are used: (1) Erreur relative globale adimensionnelle de synthése (ERGAS) index, whose English translation is relative dimensionless global error in fusion  , is a global quality index sensitive to mean shifting and dynamic range change  . The lower the ERGAS value, specially a value lower than the number of bands, the higher the spectral quality of the merged images. (2) Mean SSIM (MSSIM) index and the average quality index (Q avg ): These indices BIB002 BIB004 are used to evaluate the overall image SSIM and UIQI quality, by averaging these measures. The higher, closer to one, the value, the higher the spectral and radiometric quality of the merged images. (3) Another global measure, Q4, proposed in BIB003 depends on the individual UIQI of each band, but also on spectral distortion, embodied by the spectral angle SAM. The problem of this index is that it may not be extended to images with a number of bands greater than four.