Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion
Abstract
:1. Introduction
2. Methodology
2.1. Algorithms
2.1.1. GS and GSA
2.1.2. HR
2.1.3. HCS
- (a)
- The squares of the multispectral intensity (I2) and the PAN (P2) are calculated using Equations (7) and (8), respectively:
- (b)
- Calculate the mean (uP) and standard deviation (σp) of P2, as well as the mean (uI) and standard deviation (σP) of I2.
- (c)
- The P2 is adjusted to the mean and standard deviation of I2, using Equation (9):
- (d)
- The square root of the adjusted P2 is assigned to Iadj (i.e., ), Iadj is used in the reverse transform from HCS color space back to the original color space, using Equation (10):
2.1.4. ATWT
- (1)
- Use the à trous wavelet transform to decompose the PAN image to n wavelet planes. Usually, n = 2 or 3.
- (2)
- Add the wavelet planes (i.e., spatial details) of the decomposed PAN images to each of the spectral bands of the MS image to produce fused MS bands.
2.1.5. GLP
2.1.6. NSCT
- (a)
- Each original MS band is decomposed using 1-level NSCT to get one coarse level, , and one fine level, ;
- (b)
- The PAN band is decomposed using3-level NSCT into one coarse level, , and three fine levels, which are denoted as , , and , respectively.
- (c)
- The coefficients of each MS band, and , are up-sampled to the scale of the PAN band using the bi-linear interpolation algorithm.
- (d)
- The coarse level of the fused ith MS band, , is the up-sampled coarse level of the ith MS band , whereas the fine levels 2 () and 3 () of the fused ith MS band are the fine levels 2 () and 3 () of the PAN band.
- (e)
- The fused fine level 1, , is obtain by fusing the coefficients of the same level obtained from both the ith MS band and the PAN band. For each pixel (x, y), the coefficients of the fused fine level 1, , is determined according to Equation (18):The inverse NSCT is applied to the fused coefficients to provide the fused ith MS band.This improved version was demonstrated to provide pansharpened images with a good spectral quality.
2.2. Quality Indexes
2.2.1. ERGAS
2.2.2. SAM
2.2.3. Q2n
2.2.4. SCC
2.3. Information Indexes
2.3.1. NDVI
2.3.2. NDWI
2.3.3. MBI
- (a)
- A brightness image b is generated by setting the value of each pixel p to be the maximum digital number of the visible bands. Only the visible channels are considered due to they have the most significant contributions to the spectral property of buildings.
- (b)
- The directional white top-hat (WTH) reconstruction is employed to highlight bright structures that have a size equal to or smaller than the size of the structure element (SE), and meanwhile suppresses other dark structures in the image. WTH with linear SE is defined as Equation (34):
- (c)
- The difference morphological profiles (DMP) of white top-hat transforms are employed to model building structures in a multi-scale manner:
- (d)
- Finally, MBI is defined based on DMP using Equation (36):
3. Experimental Results
3.1. Datasets
3.2. Fusing Using the Selected Algorithms
3.3. Quality Indexes
3.3.1. Assessment for the Two Urban Images
3.3.2. Assessment for the Two Suburban Images
3.3.3. Assessment for the Two Rural Images
3.4. Information Preservation
3.4.1. CMBI
3.4.2. CNDVI and CNDWI
4. Discussion
4.1. General Performances of the Selected Pansharpening Methods
4.2. Effects for Different Spectral Ranges between the PAN and MS Bands
4.3. How to Extend the Selected Pansharpening Methods to Other HSR Satellite Images
5. Conclusions
- (1)
- Generally, the HR, GSA, GLP_ESDM and GLP_ECBD methods give better performances than the other methods, whereas the NSCT and HCS methods offer the poorest performances, for most of the test images, in terms of quality indexes and visual inspection. Some of the fusion products generated by the GS and ATWT methods show significant spectral distortions. In addition, the performances of the eight methods in terms of CMBI are consistent with those in terms of Q8 and SCC. Consequently, the HR, GSA, GLP_ESDM, and GLP_ECBD methods are good choices if the fused WV-2 images will be used for image interpretation and applications related to urban buildings. The four methods can also provide good performances for other WV-2 image scenes, for producing fused images used for image interpretation.
- (2)
- The order of the pansharpening methods in terms of CNDVI is consistent with that in terms of CNDWI. This is because both of the two indexes measure the differences between inter-band relationships of the fused image and those of the reference MS image, and both of them are related to the quality of the fused NIR1 bands. The GLP_ESDM method offers higher CNDVI and CNDWI values for I1, I2 and I5, whereas the GLP_ECBD method provides higher CNDVI and CNDWI values for I3, I4 and I6, as well as good performances in terms of quality indexes and visual inspection. Consequently, the GLP_ESDM and GLP_ECBD methods are better than other methods, if the fused WV-2 images will be used for applications related to vegetation and water-bodies. However, for this case, it is better to select a best method by comparing the indexes CNDVI and CNDWI, as well as quality indexes and visual inspection, since the GLP_ESDM and GLP_ECBD methods may give different performances for images with different land cover objects.
- (3)
- According to the experimental results of this work and the analyses the algorithms of the selected pansharpening methods, we can offer two suggestions for the fusion of images obtained by sensors similar with WV-2, such as Geoeye-1 and Worldview-3/4. Firstly, for the spectral bands with relative high correlations with the PAN band, the synthetized PAN band should be obtained using the original PAN band and the injection gains should considering the relationship between each MS band and the PAN band. The HR, GSA, GLP_ESDM, and GLP_ECBD method also can offer good performances for scenes obtained by GeoEye-1 and Worldview-3/4, for producing fused images used for interpretation and applications related to urban buildings. Secondly, for the spectral bands with relative low correlations with the PAN band, local dissimilarity between the MS and PAN bands should be considered for the fusion of these bands, i.e., the NIR band, especially for the case that the fused images will be used in applications related to vegetation and water-bodies.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Image | Location | Type | Objects | Information Indices |
---|---|---|---|---|
I1 | Beijing | Urban | High buildings, squares, roads, vegetation, shadows | MBI, NDVI |
I2 | Beijing | Urban | Moderate buildings, squares, roads, vegetation, shadows | MBI, NDVI |
I3 | Pingdingshan | Suburban | Low buildings, squares, roads, vegetation, shadows, water bodies | MBI, NDVI, NDWI |
I4 | Pingdingshan | Suburban | Low buildings, squares, roads, vegetation, shadows, water bodies | MBI, NDVI, NDWI |
I5 | Pingdingshan | Rural | Building, roads, farms, water bodies | NDVI, NDWI |
I6 | Pingdingshan | Rural | Vegetation, water bodies, bare soils | NDVI, NDWI |
Image | Method | ERGAS | SAM | Q8 | SCC |
---|---|---|---|---|---|
I1 | GS | 1.64 | 2.19 | 0.957 | 0.911 |
GSA | 1.28 | 1.92 | 0.974 | 0.908 | |
HR | 1.26 | 1.76 | 0.977 | 0.911 | |
HCS | 1.95 | 2.58 | 0.897 | 0.881 | |
ATWT | 2.09 | 2.72 | 0.879 | 0.863 | |
GLP_ESDM | 1.56 | 2.05 | 0.956 | 0.898 | |
GLP_ECBD | 1.93 | 2.36 | 0.956 | 0.888 | |
NSCT_M2 | 2.22 | 3.66 | 0.849 | 0.861 | |
EXP | 1.64 | 2.19 | 0.857 | 0.582 | |
I2 | GS | 2.70 | 3.79 | 0.909 | 0.844 |
GSA | 2.22 | 3.45 | 0.946 | 0.839 | |
HR | 2.20 | 3.14 | 0.951 | 0.849 | |
HCS | 2.87 | 3.99 | 0.850 | 0.809 | |
ATWT | 2.76 | 4.02 | 0.861 | 0.799 | |
GLP_ESDM | 2.51 | 3.47 | 0.916 | 0.824 | |
GLP_ECBD | 3.14 | 4.26 | 0.905 | 0.785 | |
NSCT_M2 | 3.04 | 5.03 | 0.831 | 0.795 | |
EXP | 3.84 | 3.99 | 0.797 | 0.499 |
Image | Method | ERGAS | SAM | Q8 | SCC |
---|---|---|---|---|---|
I3 | GS | 1.31 | 1.90 | 0.908 | 0.885 |
GSA | 1.03 | 1.76 | 0.942 | 0.883 | |
HR | 1.38 | 2.23 | 0.927 | 0.849 | |
HCS | 1.28 | 1.98 | 0.881 | 0.871 | |
ATWT | 1.21 | 1.93 | 0.907 | 0.872 | |
GLP_ESDM | 1.39 | 1.91 | 0.902 | 0.835 | |
GLP_ECBD | 1.33 | 1.91 | 0.921 | 0.856 | |
NSCT_M2 | 1.39 | 2.41 | 0.887 | 0.869 | |
EXP | 1.62 | 1.98 | 0.837 | 0.796 | |
I4 | GS | 1.74 | 2.85 | 0.871 | 0.841 |
GSA | 1.43 | 2.74 | 0.919 | 0.831 | |
HR | 1.84 | 3.25 | 0.893 | 0.779 | |
HCS | 1.74 | 3.01 | 0.850 | 0.817 | |
ATWT | 1.66 | 2.98 | 0.868 | 0.821 | |
GLP_ESDM | 1.82 | 2.82 | 0.876 | 0.796 | |
GLP_ECBD | 1.79 | 2.88 | 0.892 | 0.805 | |
NSCT_M2 | 1.88 | 3.64 | 0.843 | 0.818 | |
EXP | 2.21 | 3.01 | 0.759 | 0.669 |
Image | Method | ERGAS | SAM | Q8 | SCC |
---|---|---|---|---|---|
I5 | GS | 1.66 | 2.41 | 0.822 | 0.838 |
GSA | 1.56 | 2.66 | 0.887 | 0.799 | |
HR | 1.32 | 2.06 | 0.914 | 0.882 | |
HCS | 1.61 | 2.23 | 0.837 | 0.861 | |
ATWT | 1.59 | 2.37 | 0.853 | 0.857 | |
GLP_ESDM | 1.49 | 1.99 | 0.875 | 0.874 | |
GLP_ECBD | 1.90 | 2.45 | 0.874 | 0.845 | |
NSCT-M2 | 1.71 | 2.83 | 0.829 | 0.855 | |
EXP | 2.30 | 2.23 | 0.748 | 0.665 | |
I6 | GS | 1.88 | 2.95 | 0.762 | 0.756 |
GSA | 1.16 | 1.75 | 0.857 | 0.785 | |
HR | 1.08 | 1.60 | 0.873 | 0.812 | |
HCS | 1.05 | 1.51 | 0.735 | 0.848 | |
ATWT | 0.97 | 1.49 | 0.858 | 0.868 | |
GLP_ESDM | 1.12 | 1.52 | 0.857 | 0.809 | |
GLP_ECBD | 0.99 | 1.44 | 0.868 | 0.854 | |
NSCT-M2 | 1.10 | 1.77 | 0.820 | 0.865 | |
EXP | 1.09 | 1.51 | 0.818 | 0.849 |
Method | CMBI | CNDVI | CNDWI | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
I1 | I2 | I3 | I4 | I1 | I2 | I3 | I4 | I5 | I6 | I3 | I4 | I5 | I6 | |
GS | 0.973 | 0.972 | 0.969 | 0.934 | 0.922 | 0.877 | 0.929 | 0.891 | 0.912 | 0.946 | 0.915 | 0.858 | 0.876 | 0.950 |
GSA | 0.975 | 0.979 | 0.981 | 0.959 | 0.915 | 0.855 | 0.924 | 0.879 | 0.884 | 0.966 | 0.908 | 0.853 | 0.830 | 0.977 |
HR | 0.976 | 0.978 | 0.977 | 0.950 | 0.923 | 0.885 | 0.899 | 0.816 | 0.927 | 0.969 | 0.876 | 0.780 | 0.895 | 0.981 |
HCS | 0.962 | 0.949 | 0.923 | 0.848 | 0.926 | 0.883 | 0.917 | 0.880 | 0.940 | 0.974 | 0.911 | 0.859 | 0.917 | 0.986 |
ATWT | 0.965 | 0.956 | 0.934 | 0.882 | 0.911 | 0.849 | 0.928 | 0.890 | 0.928 | 0.975 | 0.912 | 0.851 | 0.915 | 0.986 |
GLP_ESDM | 0.965 | 0.960 | 0.945 | 0.899 | 0.928 | 0.887 | 0.915 | 0.871 | 0.942 | 0.972 | 0.901 | 0.841 | 0.922 | 0.984 |
GLP_ECBD | 0.964 | 0.957 | 0.961 | 0.926 | 0.892 | 0.811 | 0.925 | 0.876 | 0.906 | 0.977 | 0.909 | 0.851 | 0.860 | 0.988 |
NSCT_M2 | 0.965 | 0.948 | 0.905 | 0.842 | 0.860 | 0.778 | 0.898 | 0.862 | 0.896 | 0.968 | 0.873 | 0.800 | 0.871 | 0.982 |
EXP | 0.825 | 0.798 | 0.829 | 0.783 | 0.926 | 0.883 | 0.917 | 0.880 | 0.940 | 0.974 | 0.911 | 0.859 | 0.917 | 0.986 |
Image | Method | CC | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
C | B | G | Y | R | RE | NIR1 | NRI2 | Avg | ||
I1 | GS | 0.985 | 0.985 | 0.988 | 0.992 | 0.990 | 0.990 | 0.978 | 0.978 | 0.986 |
GSA | 0.987 | 0.987 | 0.991 | 0.995 | 0.993 | 0.992 | 0.978 | 0.977 | 0.988 | |
HR | 0.990 | 0.990 | 0.993 | 0.996 | 0.994 | 0.993 | 0.977 | 0.977 | 0.989 | |
HCS | 0.819 | 0.945 | 0.982 | 0.989 | 0.987 | 0.984 | 0.970 | 0.969 | 0.956 | |
ATWT | 0.849 | 0.931 | 0.985 | 0.991 | 0.986 | 0.987 | 0.974 | 0.971 | 0.959 | |
GLP_ESDM | 0.959 | 0.979 | 0.986 | 0.990 | 0.990 | 0.988 | 0.975 | 0.973 | 0.980 | |
GLP_ECBD | 0.982 | 0.982 | 0.986 | 0.989 | 0.986 | 0.985 | 0.967 | 0.959 | 0.980 | |
NSCT_M2 | 0.805 | 0.906 | 0.985 | 0.993 | 0.987 | 0.988 | 0.971 | 0.965 | 0.950 | |
EXP | 0.952 | 0.948 | 0.944 | 0.942 | 0.939 | 0.928 | 0.917 | 0.916 | 0.936 | |
I3 | GS | 0.958 | 0.957 | 0.962 | 0.967 | 0.962 | 0.952 | 0.899 | 0.903 | 0.945 |
GSA | 0.977 | 0.977 | 0.986 | 0.990 | 0.984 | 0.977 | 0.906 | 0.906 | 0.963 | |
HR | 0.976 | 0.975 | 0.983 | 0.986 | 0.980 | 0.969 | 0.830 | 0.823 | 0.940 | |
HCS | 0.836 | 0.946 | 0.977 | 0.979 | 0.969 | 0.968 | 0.892 | 0.893 | 0.933 | |
ATWT | 0.918 | 0.932 | 0.980 | 0.982 | 0.975 | 0.971 | 0.900 | 0.898 | 0.945 | |
GLP_ESDM | 0.919 | 0.964 | 0.978 | 0.985 | 0.980 | 0.966 | 0.842 | 0.844 | 0.935 | |
GLP_ECBD | 0.962 | 0.963 | 0.975 | 0.982 | 0.975 | 0.965 | 0.876 | 0.861 | 0.945 | |
NSCT_M2 | 0.886 | 0.906 | 0.980 | 0.978 | 0.969 | 0.968 | 0.872 | 0.863 | 0.928 | |
EXP | 0.922 | 0.921 | 0.921 | 0.923 | 0.920 | 0.910 | 0.886 | 0.885 | 0.911 | |
I5 | GS | 0.958 | 0.951 | 0.945 | 0.947 | 0.953 | 0.908 | 0.868 | 0.875 | 0.926 |
GSA | 0.969 | 0.964 | 0.963 | 0.962 | 0.967 | 0.981 | 0.949 | 0.957 | 0.964 | |
HR | 0.968 | 0.966 | 0.974 | 0.968 | 0.967 | 0.984 | 0.954 | 0.965 | 0.968 | |
HCS | 0.739 | 0.892 | 0.954 | 0.966 | 0.967 | 0.984 | 0.962 | 0.973 | 0.930 | |
ATWT | 0.901 | 0.924 | 0.976 | 0.967 | 0.965 | 0.987 | 0.966 | 0.975 | 0.958 | |
GLP_ESDM | 0.913 | 0.962 | 0.975 | 0.970 | 0.970 | 0.984 | 0.949 | 0.963 | 0.961 | |
GLP_ECBD | 0.942 | 0.935 | 0.959 | 0.967 | 0.968 | 0.985 | 0.964 | 0.974 | 0.962 | |
NSCT_M2 | 0.858 | 0.888 | 0.970 | 0.960 | 0.957 | 0.981 | 0.957 | 0.968 | 0.943 | |
EXP | 0.940 | 0.932 | 0.923 | 0.925 | 0.931 | 0.976 | 0.966 | 0.974 | 0.946 | |
I6 | GS | 0.957 | 0.959 | 0.967 | 0.967 | 0.966 | 0.948 | 0.802 | 0.800 | 0.921 |
GSA | 0.970 | 0.974 | 0.985 | 0.982 | 0.980 | 0.963 | 0.750 | 0.747 | 0.919 | |
HR | 0.979 | 0.980 | 0.987 | 0.987 | 0.985 | 0.969 | 0.858 | 0.854 | 0.950 | |
HCS | 0.879 | 0.966 | 0.978 | 0.971 | 0.964 | 0.936 | 0.848 | 0.846 | 0.924 | |
ATWT | 0.896 | 0.924 | 0.980 | 0.979 | 0.971 | 0.950 | 0.858 | 0.862 | 0.928 | |
GLP_ESDM | 0.905 | 0.962 | 0.976 | 0.978 | 0.979 | 0.955 | 0.869 | 0.848 | 0.934 | |
GLP_ECBD | 0.956 | 0.960 | 0.972 | 0.971 | 0.967 | 0.945 | 0.790 | 0.829 | 0.924 | |
NSCT_M2 | 0.873 | 0.905 | 0.982 | 0.978 | 0.968 | 0.953 | 0.801 | 0.829 | 0.911 | |
EXP | 0.904 | 0.899 | 0.893 | 0.899 | 0.900 | 0.788 | 0.747 | 0.748 | 0.848 |
© 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
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Li, H.; Jing, L.; Tang, Y. Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion. Sensors 2017, 17, 89. https://doi.org/10.3390/s17010089
Li H, Jing L, Tang Y. Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion. Sensors. 2017; 17(1):89. https://doi.org/10.3390/s17010089
Chicago/Turabian StyleLi, Hui, Linhai Jing, and Yunwei Tang. 2017. "Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion" Sensors 17, no. 1: 89. https://doi.org/10.3390/s17010089
APA StyleLi, H., Jing, L., & Tang, Y. (2017). Assessment of Pansharpening Methods Applied to WorldView-2 Imagery Fusion. Sensors, 17(1), 89. https://doi.org/10.3390/s17010089