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MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, and M. Selva
Abstract This work presents a multiresolution framework for merging a multispectral image having an arbitrary number of bands with a higher-resolution panchromatic observation. The fusion method relies on the generalized Laplacian pyramid (GLP), which is a multiscale, oversampled structure. The goal is to selectively perform injection of spatial frequencies from an image to another with the constraint of thoroughly retaining the spectral information of the coarser data. The novel idea is that a model of the modulation transfer functions (MTF) of the multispectral scanner is exploited to design the GLP reduction filter. Thus, the interband structure model (IBSM), which is calculated at the coarser scale, where both MS and PAN data are available, can be extended to the finer scale, without the drawback of the poor enhancement occurring when MTFs are assumed to be ideal filters. Experiments carried out on QuickBird data demonstrate that a superior spatial enhancement, besides the spectral quality typical of injection methods, is achieved by means of the MTF-adjusted fusion.
Introduction Image fusion techniques, originally devised to allow integration of different information sources, may take advantage of the complementary spatial/spectral resolution characteristics typical of remote sensing imagery. When exactly three multispectral (MS) bands are concerned, the most straightforward fusion method is to resort to an intensity-hue-saturation (IHS) transformation (Welch and Ehlers, 1987; Carper et al., 1990). This procedure is equivalent to inject, i.e., add, the difference between the sharp panchromatic (PAN) image and the smooth intensity into the resampled MS bands (Tu et al., 2001). Since the PAN image (histogram-matched to the intensity component) does not generally have same local radiometry as the latter, when the fusion result is displayed in color composition, large spectral distortion (color changes) may be noticed. When more than three spectral bands are available, IHS fusion may be applied to three consecutive spectral components at a time, or better the IHS transformation may be replaced with principal component analysis (PCA) (Shettigara, 1992). Generally speaking, if the spectral responses of the MS bands are not perfectly overlapped with the bandwidth of PAN, as it
B. Aiazzi, S. Baronti, and M. Selva are with the Institute of Applied Physics “Nello Carrara” of the National Research Council IFAC-CNR, Florence, Italy. L. Alparone is with the Department of Electronics and Telecommunications, University of Florence, Florence, Italy (
[email protected]). A. Garzelli is with the Department of Information Engineering, University of Siena, Siena, Italy. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
happens to Ikonos-2 and QuickBird, IHS- and PCA-based methods may yield very poor results in terms of spectral fidelity (Tu et al., 2004). To overcome this inconvenience, methods based on injecting spatial details only, taken from the PAN image without resorting to IHS transformation, have been introduced and have demonstrated superior performance. Multiresolution analysis (MRA) (Mallat, 1989) provides effective tools, like wavelets and pyramids (Aiazzi et al., 2002), to carry out image merge tasks. However, in the case of highpass detail injection, spatial distortions, typically ringing or aliasing effects, originating shifts or blur of contours and textures may occur (Yocky, 1996). These drawbacks, which may be as much annoying as spectral distortions, are emphasized by mis-registration between MS and PAN data, especially if the MRA underlying detail injection is not shift-invariant (Aiazzi et al., 2002; Gonzáles Audícana et al., 2004). The goal of an advanced fusion method is to increase spectral information by unmixing the coarse MS pixels through the sharp PAN image. This additional task requires the definition of a model establishing how the missing highpass information to be injected is extracted from the PAN image. It may be accomplished either in the domain of approximations between each of the resampled MS bands and a lowpass version of the PAN image having the same spatial frequency content as the MS bands, or in that of medium frequency details, in both cases by measuring local matching (Ranchin et al., 2003). High frequency details are not available for MS bands, and must be inferred through the model, starting from those of PAN. Quantitative results of data merge are provided thanks to the availability of reference originals obtained either by simulating the target sensor by means of high-resolution data from an airborne platform, or by degrading all available data to a coarser resolution and carrying out merge from such data. In practical cases this strategy is not feasible. The idea behind this approach, however, is that algorithms optimized to yield best results at coarser scales, i.e., on spatially degraded data, should still be optimal when the data are considered at finer scales, as it happens in practice. This assumption may be reasonable in general, but unfortunately does not hold true for very high-resolution data, especially when a highly detailed urban environment is concerned. The reason for this behavior lies in the characteristics of the Modulation Transfer Function (MTF) of the imaging system. Any interscale injection model should take into account that the MTF of real systems is generally bell shaped, and its magnitude value at the cutoff Nyquist
Photogrammetric Engineering & Remote Sensing Vol. 72, No. 5, May 2006, pp. 591–596. 0099-1112/06/7205–0591/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing May 2006
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frequency is far lower than 0.5, to prevent aliasing. Furthermore, the MTFs of the MS sensors may be significantly different from one another in terms of decay rate, and especially are different from that of the PAN sensor. Hence, models empirically optimized at a coarser scale on data degraded by means of digital filters that are close to be ideal, may yield little enhancement when are utilized at the finer scale. In this work, an assumed model of MTF is exploited to improve results of fusion schemes based on MRA, provided that the latter is not critically subsampled. MTF-tailored Multiresolution Analysis Figure 1a shows the theoretical MTF of an imaging system. As a first approximation, the MTF is related to the optical point spread function (PSF) of the imaging system. In principle, two spectral replicas originated by 2D sampling of the radiance signal with sampling frequency along- and across-track equal to the Nyquist rate, should cross each
(a)
(b)
Figure 1. (a) ideal (isotropic) MTF with magnitude equal to 0.5 at cutoff Nyquist frequency; (b) typical (anisotropic) MTF with magnitude 0.2 at cutoff Nyquist frequency (across-track). All frequency scales are normalized to sampling frequency, or Nyquist rate (twice the Nyquist frequency).
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other at the Nyquist frequency (half of Nyquist rate) with magnitude values equal to 0.5. However, the scarce selectivity of the response prevents from using a Nyquist frequency with magnitude equal to 0.5. As a tradeoff between maximum spatial resolution and minimum aliasing of the sampled signal, the Nyquist frequency is usually chosen such that the corresponding magnitude value is around 0.2. This situation is depicted in Figure 1b portraying the true MTF of an MS channel, which also depends on the platform motion (narrower along track), on atmospheric effects and on the finite sampling. A different situation occurs for the MTF of the PAN channel, whose extent is mainly dictated by diffraction limits (at least for instruments with resolution around 1 m). In that case, the cutoff magnitude may be even lower (e.g., 0.1), and the appearance of the acquired images is rather blurred. However, whereas the enhancing PAN image that is commonly available has been already processed for MTF restoration, the MS bands cannot be preprocessed analogously because of SNR constraints. In fact, restoration implies a kind of inverse filtering that has the effect of increasing the noisiness of the data. Eventually, the problem may be stated in the following terms: an MS band resampled at the finer scale of the PAN image lacks high spatial frequency components, that may be inferred from the PAN image using a suitable interscale injection model. If the highpass filter used to extract such frequency components from the PAN image is taken such as to approximate the complement of the MTF of the MS band to be enhanced, then the high frequency components, that are present in the MS band but have been damped by its MTF, can be restored. Otherwise, if spatial details are extracted from the PAN image by using a filter having normalized frequency cutoff at exactly the scale ratio between PAN and MS (e.g., 1⁄4 for 1 m PAN and 4 m MS), such frequency components will not be injected. This occurs with critically subsampled wavelet decompositions, whose filters are constrained to cutoff at exactly an integer fraction (usually a power of two) of the Nyquist frequency of PAN data, corresponding to the scale ratio between PAN and MS. An attractive characteristic of the redundant pyramid and wavelet decompositions proposed by the authors (Aiazzi et al., 2002; Garzelli and Nencini, 2005) is that the lowpass reduction filter used to analyze the PAN image may be easily designed such that it matches the MTF of the band into which the details extracted will be injected. Figure 2a shows examples for three values of magnitude cutoff. The resulting benefit is that the restoration of spatial frequency content of the MS bands is provided by the MRA of PAN through the injection model. A forerunner of this rationale was the work by Núñez et al. (1999), even if no considerations on MTF were made. Figure 2b shows that the Gaussian-like frequency response of the cubic-spline wavelet filter used to generate the “à trous” wavelet transform (ATWT) matches the shape of the MTF of a typical V-NIR band, with cutoff magnitude value equal to 0.185. The complementary highpass filter, yielding the detail level to be injected for 1:4 fusion, retains a greater amount of spatial frequency components than an ideal filter, such that used by the standard GLP and ATWT (Aiazzi et al., 2002; Garzelli and Nencini, 2005), thereby resulting in a greater spatial enhancement. GLP-based Fusion Scheme Figure 3 shows the flowchart of the general case of MS PAN fusion with 1:p scale ratio, based on the GLP (Alparone et al., 2003). In this context, emphasis will be given to the detail injection model, indicated as interband structure model (IBSM), for which two improvements of the solutions PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
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Figure 3. Flowchart of multiresolution fusion based on generalized Laplacian pyramid and suitable for merging multispectral and panchromatic image data, whose integer scale ratio is p 1. p p denotes down-sampling by p; q p denotes up-sampling by p; rp is the MTFmatching p-reduction lowpass filter with cutoff at 1/p of the spectrum extent; ep is ideal p-expansion lowpass filter with 1/p cutoff. (a)
vector notation, S(i,j) {S(l)(i,j), l 1, . . . , L}. Let ˜ (i,j ) e G˜ (l )(i,j ), l 1, p , L f denote the pixel expanded G image; let also W0MS(i,j) S(i,j) W0(P)(i,j) denote the MS detail vector to be injected. In order to minimize the SAM distortion between resampled MS bands and fused products, the injected detail vector at pixel position ~ (i,j) must be parallel to the resampled MS vector, i.e., to G(i,j). At the same time each component (l)(i,j) should be designed so as to minimize the radiometric distortion when the detail compo~ nent W0(l)(i,j) is injected into G (l)(i,j). Starting from the vector merge relationship MS
ˆ (i,j) G ˜ (i,j) s(i,j) # W0(P)(i,j). G
we define the lth components of S(i,j) as G˜ (l) (i,j) # (3) (l) , l 1, p ,L s (i,j) (i,j) G˜ 2(P) (i,j ) ~ in which G 2(P)(i,j) denotes the approximation of the PAN image produced by the equivalent lowpass filter, and the coefficient (i,j), which does not depend on the band index l, is defined in such a way that the length of the fused vector is statistically close to that of the (unavailable) true high-resolution vector, as the ratio between average local standard deviations of resampled MS bands and local standard deviation of lowpass approximation of PAN:
(b)
Figure 2. 1D frequency responses of equivalent filters for separable 2D 1:4 MRA (lowpass and highpass filters generate approximation and detail, respectively). (a) sample MTF-adjusted Gaussian-shaped filters with magnitudes 0.1, 0.2, and 0.3 at cutoff Nyquist frequency; (b) GLP-generating filters (dashed) and cubic spline filter for ATWT generation (solid).
introduced by Garzelli and Nencini (2005) and by Aiazzi et al. (2002) will be described.
L
Enhanced Spectral Distortion Minimizing (ESDM) Model Given two spectral vectors v and vˆ both having L components, in which v {v1, v2, . . . , vL} is the original spectral pixel vector vl G(l)(i,j), while vˆ {ˆv1, vˆ 2, . . . ,ˆvL} is the distorted vector obtained by applying fusion to the coarser resolution MS data, i.e., vˆ l Gˆ (l)(i,j); the spectral angle mapper (SAM) denotes the absolute value of the spectral angle between the two vectors: SAM(v,vˆ ) ar cosa
v,vˆ b. v 2 # vˆ 2
The Enhanced Spectral Distortion Minimizing (ESDM) model is both space- and spectrally-varying; stated with a PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
(2)
(1)
1 a varCG˜ (l) D(i,j ) L l1 (i,j) G varC G˜ 2(P) D (i,j )
(4)
In the original SDM model (Garzelli and Nencini, 2005) the correcting factor , aimed at mitigating the unlikely overenhancement, mainly noticed on vegetated areas, was missing. From Equations 2 and 3 it stems that ˜ ˜ # (P) G˜ Gˆ G˜ CG˜ WMS 0 D G CG s W0 D (P) ˜ G ˜ c 1 # W0 d 0 G (P) G˜ 2
(5)
in which stands for vector product, and indexes (i,j) have been omitted. Equation 5 states that the spectral angle (SAM) May 2006
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is unchanged when a vector pixel in the expanded MS ~ ˆ image, G(i,j), is enhanced to yield the fused product, ~ G (i,j), because the upgrade W0MS(i,j) is always parallel to G(i,j). Enhanced Context-Based (ECB) Model The Enhanced Context-Based (ECB) injection model rules the transformation in the detail from the PAN image to the target lth MS band. Although substantially similar to the model introduced by Aiazzi et al. (2002), it lacks the threshold decision on injection, which is crucial for obtaining a proper spatial enhancement. The ECB model may be stated as: # (P) W0(l)(i,j) a(l) C (i,j ) W0 (i,j ), l 1, p , L.
(6)
The space-varying model C (i,j) is calculated between ~ the lth MS band resampled to the scale of the PAN image, G (l), and the approximation of the PAN image at the resolution of ~ the MS bands G 2(P), (l)
a(l) C (i,j) min •
l,P (i,j)
#
l,P
G(l ) (i,j)
G(P) (i,j)
,c ¶
(7)
2
in which ~ l,P(i,j)~is the local linear correlation coefficient between G (l) and G 2(P) calculated on a square sliding window of size N N centered on pixel (i,j); r l,P is the global one and is constant throughout. The ECB model is uniquely defined by the window size N depending on the spatial resolutions and scale ratio of the images to be merged, as well as on the landscape characteristics (typically, 7 N 11). A clipping constant c was introduced to avoid numerical instabilities (2 c 3).
Experimental Results Quality Assessment of Fusion Products Quality assessment of PAN-sharpened MS images is a hard task (Chavez Jr. et al., 1991; Wald et al., 1997). Even when spatially degraded MS images are processed for PAN-sharpening, and therefore reference MS images are available for comparisons, assessment of fidelity to the reference usually requires computation of a number of different indexes. Examples are correlation coefficient (CC) between each band of the fused and reference MS images, bias in the mean, root mean square error (RMSE) and average SAM, which measures the spectral distortion introduced by the fusion process. In fact, a value of SAM (Equation 1) equal to zero denotes absence of spectral distortion, but possible radiometric distortion (the two pixel vectors are parallel but have different lengths). SAM is measured in either degrees or radians and is usually averaged over the whole image to yield a global measurement of spectral distortion. Wald et al. (1997) proposed an error index that offers a global picture of the quality of a fused product. This error is called ERGAS, which means relative dimensionless global error in synthesis, and is given by: dh ERGAS 100 dl
1 GL
L
aa
l1
RMSE(l ) 2 b (l )
(8)
dh is the ratio between pixel sizes of PAN and MS, e.g., dl 1 /4 for Ikonos and QuickBird data, (l) is the mean (average)
where
of the lth band, and L is the number of bands. An image quality index suitable for MS images having four spectral bands was recently proposed by Alparone et al. 594
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(2004) for assessing PAN-sharpening methods. The quality index Q4 is a generalization to four-band images of the Q index (Wang and Bovik, 2002), which can be applied only to monochrome images. Q4 is obtained through the use of CC between hypercomplex numbers, or quaternions, representing spectral pixel vectors. Q4 is made of three different factors: the first is the modulus of the hypercomplex CC between the two spectral pixel vectors and is sensitive both to loss of correlation and to spectral distortion between the two MS data sets. The second and third terms, respectively, measure contrast changes and mean bias on all bands simultaneously. The modulus of the hypercomplex CC measures the alignment of spectral vectors. Therefore, its low value may detect when radiometric distortion is accompanied by spectral distortion. Thus, both radiometric and spectral distortions may be encapsulated in a unique parameter. All statistics are calculated as averages on N N blocks, either N 16 or N 32. Eventually, Q4 is averaged over the whole image to yield the global score index. The highest value of Q4, attained if and only if the test MS image is equal to the reference, is one; the lowest value is zero. Performance Comparison of Image Fusion Algorithms The proposed fusion procedures have been assessed on very high-resolution image data collected on 23 June 2002 at 10:25:59 GMT 2 by the QuickBird spaceborne MS scanner on the urban area of Pavia, Italy. The four MS bands, namely B1, B2, B3, and B4, span the visible and near-infrared (NIR) wavelengths and are spectrally disjoint. The PAN band embraces the interval 450 900 nm. All data are resampled to uniform ground resolutions of 2.8 m and 0.7 m GSD for MS and PAN. The MS and PAN data are spatially degraded by 4, to yield 2.8 m PAN and 11.2 m MS, and are used to synthesize the MS bands at 2.8 m. Thus, the true MS data at 2.8 m are available for objective distortion measurements. As it is known (Wald et al., 1997), the simulation on spatially degraded data is aimed at adjusting the fusion algorithm in such a way that once the best objective fidelity score with the reference originals have been obtained, the same algorithm will run on the true higher-resolution data and produce the best results. Rather than trying to obtain the best absolute scores, it is important to measure how much a fusion method is capable of improving the quality of the fused product with respect to that of the resampled MS data, which constitute its starting point. Therefore, both an ideal filter and MTF-matched filters were used for prefiltering the MS data before decimation. In the former case the fusion algorithm uses a conventional MRA. In the latter, the reduction filter of GLP matches the MTF of each MS band, as previously described. PAN data were always degraded with the ideal filter. In order to easily evaluate the quality increment, either with or without MTF adjustment of MRA, only global scores, like SAM, ERGAS, and Q4 will be used. A comparison of GLP-based methods with ESDM and ECB injection models was carried out with two methods: multiresolution IHS (Núñez et al., 1999) with additive model (AWL), based on ATWT, and highpass filtering (HPF) technique (Chavez Jr. et al., 1991), with either 5 5 or 7 7 box filter for obtaining ideally- or MTF-degraded MS bands. Although formally different (AWL operates in IHS domain, HPF in the plain spectral domain), the sole practical difference between HPF and AWL is the lowpass filter used for generating the lowest-resolution approximation: a box filter for the former, a cascade of progressively upsampled cubic-spline filters (with frequency response shown in Figure 2b), for the latter. Unlike the other schemes compared, both HPF and AWL do not exploit a space-varying IBSM. Apart from details on filter and injection models, HPF and AWL may be described by the flowchart shown in Figure 3. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
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TABLE 1. AVERAGE CUMULATIVE QUALITY INDEXES BETWEEN 2.8 M MS SPECTRAL VECTORS AND THOSE OBTAINED FROM FUSION OF 11.2 M MS WITH 2.8 M PAN. MS BANDS DEGRADED WITH EITHER IDEAL OR MTF-MATCHED FILTERS. INDICATES THE DIFFERENCE BETWEEN THE CURRENT PARAMETER AND THE CORRESPONDING VALUE ACHIEVED BY PLAIN RESAMPLING (EXP) Ideal Q4 SAM(deg.) ERGAS MTF Q4 SAM (deg.) ERGAS
EXP
AWL
0.756 2.14° 1.760
0.827 ( 0.071) 2.59° ( 0.45°) 1.611 ( 0.149)
EXP
AWL
0.641 2.46° 1.980
0.815 ( 0.174) 2.53° ( 0.07°) 1.647 ( 0.333)
ESDM 0.878 ( 0.122) 2.14° ( 0.00°) 1.470 ( 0.290) ESDM 0.819 ( 0.178) 2.46° ( 0.00°) 1.608 ( 0.372)
The parameters in Table 1 measuring the global distortion of pixel vectors, either radiometric (ERGAS, which should be as low as possible) or spectral (SAM, which should be as low as possible), and both radiometric and spectral (Q4, which should be as close as possible to one) will give a comprehensive measurement of fusion performance. EXP denotes the case in which the degraded MS data are resampled through the 23-taps expansion filter of GLP, and no injection of details is made. Depending on whether degradation is ideal or MTF-based, the quality of all fusion products, including the resampled MS bands (EXP) significantly changes, being lower in the latter case, because the startup
ECB 0.862 ( 0.106) 1.90° ( 0.24°) 1.695 ( 0.065) ECB 0.834 ( 0.193) 2.13° ( 0.33°) 1.589 ( 0.391)
HPF (5 5) 0.848 ( 0.092) 2.51° ( 0.37°) 2.012 ( 0.252) HPF (7 7) 0.781 ( 0.140) 2.81° ( 0.35°) 2.132 ( 0.152)
data are poorer in both spectral and geometrical content. Only AWL is little penalized in the latter case, because its MRA implicitly embeds the MTF model. The SAM attained by ECB is lower than that of ESDM (identical to that of resampled MS data), thanks to the unmixing capabilities of the former compared to the latter. It is noteworthy that the fused images produced by MTF adjusted methods exhibit a quality increment with respect to that of resampled MS data, that is greater than that noticed when degradation is ideal and therefore methods do not need MTF adjustment. Eventually, Plate 1 displays true color compositions of the resampled 2.8 m MS bands and of the spatially enhanced MS
Plate 1. Examples of full-scale spatial enhancement of fusion algorithms displayed as 512 512 true color compositions (B3, B2, and B1 as R-G-B channels) at 0.7 m pixels spacing. (a) original MS bands (2.8 m) resampled to the scale of PAN image (0.7 m); (b) AWL method; (c) GLP-ECB without MTF adjustment; (d) GLP-ECB with MTF adjustment; (e) GLP-ESDM without MTF adjustment; and (f) GLP-ESDM with MTF adjustment.
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bands, all at 0.7 m. True-color visualization has been deliberately chosen, because PAN-sharpening of MS bands falling partly outside the bandwidth of PAN, as in the case of the blue band (B1), is particularly critical (Zhang, 2004). HPF yields a fused image very similar to that of AWL, even though slightly less accurate. Therefore, its result is not portrayed in Plate 1. The ESDM and ECB models are applied to GLP, achieved either without or with MTF-matched filters. A visual inspection highlights that all the spectral signatures of the original MS data are carefully incorporated in the sharpened bands. Thanks to the two injection models, the texture of the canopies, which is highlighted by the PAN image, but mostly derives from the NIR band, which is outside the visible wavelengths, appears to be damped in the ESDM and ECB fusion products. AWL, which implicitly accounts for the MTF in the MRA, is geometrically rich and detailed, but over-enhanced, especially on vegetated areas. A visual analysis carried out, e.g., on the small circular square surrounded by trees, reveals that the methods exploiting MTF-tailored MRA yield sharper and cleaner geometrical structures than those which use ideal filters for MRA.
Conclusions It has been demonstrated that image fusion methods based on MRA can be improved if the frequency analysis of PAN is carried out by using digital filters with frequency responses matching a model of the MTF of the imaging system. Although specific examples of injection models have been used, the proposed approach can be used with any fusion method, provided that its MRA is not critically subsampled. In this way, methods empirically optimized at a coarser scale, i.e., on spatially degraded data whose reference originals are available for performance assessment, are still effective when fusion is carried out at the actual scale. Under this perspective, a degree of freedom in Wald’s protocol of quality evaluation (Wald et al., 1997) has been fixed. Despite the claim that fusion performances may be inferred from a coarser scale to the actual scale, it is never mentioned which kind of filter must be used for image degradation. Now it is possible to state that the filter should match the shape of the analog filter through which the radiance is conveyed to the digitizer. In conclusion, the present paper wishes also to contribute to explain why so many image fusion algorithms exist, but so few are currently utilized. As a notable example we wish to remind the Gram-Schmidt (GS) spectral sharpening method (Laben and Brower, 2000) implemented in ENVI®, which does not exploits MRA, but a kind of orthogonal component substitution reminiscent of PC substitution (Shettigara, 1992). The visually stunning results of GS and of other schemes not based on MRA is the proof that users accept a minimal spectral distortion, which is implicit in all component-substitution methods, because the spatial enhancement attainable with MRA-based methods is generally not satisfactory, at least so far.
References Aiazzi, B., L. Alparone, S. Baronti, and A. Garzelli, 2002. Contextdriven fusion of high spatial and spectral resolution data based
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on oversampled multiresolution analysis, IEEE Transactions on Geoscience and Remote Sensing, 40(10):2300–2312. Alparone, L., B. Aiazzi, S. Baronti, and A. Garzelli, 2003. Sharpening of very high resolution images with spectral distortion minimization, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Volume I, pp. 458–460. Alparone, L., S. Baronti, A. Garzelli, and F. Nencini, 2004. A global quality measurement of pan-sharpened multispectral imagery, IEEE Geoscience and Remote Sensing Letters, 1(4):313–317. Carper, W., T. Lillesand, and R. Kiefer, 1990. The use of intensityhue-saturation transformations for merging SPOT panchromatic and multispectral image data, Photogrammetric Engineering & Remote Sensing, 56(4):459–467. Chavez Jr., P.S., S.C. Sides, and J.A. Anderson, 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic, Photogrammetric Engineering & Remote Sensing, 57(3):295–303. Garzelli, A., and F. Nencini, 2005. Interband structure modeling for pan-sharpening of very high resolution multispectral images, Information Fusion, 6(3):213–224. Gonzáles Audícana, M., J.L. Saleta, R. García Catalán, and R. García, 2004. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 42(6):1291–1299. Laben, C.A., and B.V. Brower, 2000. Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pansharpening, U.S. Patent No. 6,011,875, Eastman Kodak Company. Mallat, S., 1989. A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 11(7):674–693. Núñez, J., X. Otazu, O. Fors, A. Prades, V. Palà, and R. Arbiol, 1999. Multiresolution-based image fusion with additive wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 37(3):1204–1211. Ranchin, T., B. Aiazzi, L. Alparone, S. Baronti, and L. Wald, 2003. Image fusion – The ARSIS concept and some successful implementation schemes, ISPRS Journal of Photogrammetry and Remote Sensing, 58(1–2):4–18. Shettigara, V.K., 1992. A generalized component substitution technique for spatial enhancement of Multispectral images using a higher resolution data set, Photogrammetric Engineering & Remote Sensing, 58(5):561–567. Tu, T.-M., P.S. Huang, C.-L. Hung, and C.-P. Chang, 2004. A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery, IEEE Geoscience and Remote Sensing Letters, 1(4):309–312. Tu, T.-M., S.-C. Su, H.-C. Shyu, and P.S. Huang, 2001. A new look at IHS-like image fusion methods, Information Fusion, 2(3):177–186. Wald, L., T. Ranchin, and M. Mangolini, 1997. Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images, Photogrammetric Engineering & Remote Sensing, 63(6):691–699. Wang, Z., and A.C. Bovik, 2002. A universal image quality index, IEEE Signal Processing Letters, 9(3):81–84. Welch, R., and M. Ehlers, 1987. Merging multiresolution SPOT HRV and Landsat TM data, Photogrammetric Engineering & Remote Sensing, 53(3):301–303. Yocky, D.A., 1996. Artifacts in wavelet image merging, Optical Engineering, 35(7):2094–2101. Zhang, Y., 2004. Understanding image fusion, Photogrammetric Engineering & Remote Sensing, 70(6):657–661.
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