1i.pansharpen(1) Grass User's Manual i.pansharpen(1)
2
6 i.pansharpen - Image fusion algorithms to sharpen multispectral with
7 high-res panchromatic channels
8
10 imagery, fusion, sharpen, Brovey, IHS, HIS, PCA
11
13 i.pansharpen
14 i.pansharpen --help
15 i.pansharpen [-sl] red=name green=name blue=name pan=name output=base‐
16 name method=string [--overwrite] [--help] [--verbose] [--quiet]
17 [--ui]
18 Flags:
20 -s
21 Serial processing rather than parallel processing
22 -l
24 Rebalance blue channel for LANDSAT
25 --overwrite
27 Allow output files to overwrite existing files
28 --help
30 Print usage summary
31 --verbose
33 Verbose module output
34 --quiet
36 Quiet module output
37 --ui
39 Force launching GUI dialog
40 Parameters:
42 red=name [required]
43 Name of raster map to be used for <red>
44 green=name [required]
46 Name of raster map to be used for <green>
47 blue=name [required]
49 Name of raster map to be used for <blue>
50 pan=name [required]
52 Name of raster map to be used for high resolution panchromatic
53 channel
54 output=basename [required]
56 Name for output basename raster map(s)
57 method=string [required]
59 Method for pan sharpening
60 Options: brovey, ihs, pca
61 Default: ihs
62
64 i.pansharpen uses a high resolution panchromatic band from a multispec‐
65 tral image to sharpen 3 lower resolution bands. The 3 lower resolution
66 bands can then be combined into an RGB color image at a higher (more
67 detailed) resolution than is possible using the original 3 bands. For
68 example, Landsat ETM has low resolution spectral bands 1 (blue), 2
69 (green), 3 (red), 4 (near IR), 5 (mid-IR), and 7 (mid-IR) at 30m reso‐
70 lution, and a high resolution panchromatic band 8 at 15m resolution.
71 Pan sharpening allows bands 3-2-1 (or other combinations of 30m resolu‐
72 tion bands like 4-3-2 or 5-4-2) to be combined into a 15m resolution
73 color image.
74 i.pansharpen offers a choice of three different ’pan sharpening’ algo‐
75 rithms: IHS, Brovey, and PCA.
76 For IHS pan sharpening, the original 3 lower resolution bands, selected
77 as red, green and blue channels for creating an RGB composite image,
78 are transformed into IHS (intensity, hue, and saturation) color space.
79 The panchromatic band is then substituted for the intensity channel
80 (I), combined with the original hue (H) and saturation (S) channels,
81 and transformed back to RGB color space at the higher resolution of the
82 panchromatic band. The algorithm for this can be represented as: RGB ->
83 IHS -> [pan]HS -> RGB.
84 With a Brovey pan sharpening, each of the 3 lower resolution bands and
85 panchromatic band are combined using the following algorithm to calcu‐
86 late 3 new bands at the higher resolution (example for band 1):
87 band1
88 new band1 = ----------------------- * panband
89 band1 + band2 + band3
90 In PCA pan sharpening, a principal component analysis is performed on
91 the original 3 lower resolution bands to create 3 principal component
92 images (PC1, PC2, and PC3) and their associated eigenvectors (EV), such
93 that:
94 band1 band2 band3
95 PC1: EV1-1 EV1-2 EV1-3
96 PC2: EV2-1 EV2-2 EV2-3
97 PC3: EV3-1 EV3-2 EV3-3
98 and
99 PC1 = EV1-1 * band1 + EV1-2 * band2 + EV1-3 * band3 - mean(bands 1,2,3)
100 An inverse PCA is then performed, substituting the panchromatic band
101 for PC1. To do this, the eigenvectors matrix is inverted (in this case
102 transposed), the PC images are multiplied by the eigenvectors with the
103 panchromatic band substituted for PC1, and mean of each band is added
104 to each transformed image band using the following algorithm (example
105 for band 1):
106 band1’ = pan * EV1-1 + PC2 * EV2-1 + PC3 * EV3-1 + mean(band1)
107 The assignment of the channels depends on the satellite. Examples of
108 satellite imagery with high resolution panchromatic bands, and lower
109 resolution spectral bands include Landsat 7 ETM, QuickBird, and SPOT.
110
112 The module currently only works for 8-bit images.
113 The command temporarily changes the computational region to the high
114 resolution of the panchromatic band during sharpening calculations,
115 then restores the previous region settings. The current region coordi‐
116 nates (and null values) are respected. The high resolution panchromatic
117 image is histogram matched to the band it is replaces prior to substi‐
118 tution (i.e., the intensity channel for IHS sharpening, the low res
119 band selected for each color channel with Brovey sharpening, and the
120 PC1 image for PCA sharpening).
121 By default, the command will attempt to employ parallel processing,
122 using up to 3 cores simultaneously. The -s flag will disable parallel
123 processing, but does use an optimized r.mapcalc expression to reduce
124 disk I/O.
125 The three pan-sharpened output channels may be combined with d.rgb or
126 r.composite. Colors may be optionally optimized with i.colors.enhance.
127 While the resulting color image will be at the higher resolution in all
128 cases, the 3 pan sharpening algorithms differ in terms of spectral
129 response.
130
132 Pan sharpening comparison example
133 Pan sharpening of a Landsat image from Boulder, Colorado, USA:
134 # R, G, B composite at 30m
135 g.region raster=p034r032_7dt20010924_z13_10 -p
136 d.rgb b=p034r032_7dt20010924_z13_10 g=lp034r032_7dt20010924_z13_20
137 r=p034r032_7dt20010924_z13_30
138 # i.pansharpen with IHS algorithm
139 i.pansharpen red=p034r032_7dt20010924_z13_30 green=p034r032_7dt20010924_z13_20
140 blue=p034r032_7dt20010924_z13_10 pan=p034r032_7dp20010924_z13_80
141 output=ihs321 method=ihs
142 # ... likewise with method=brovey and method=pca
143 # display at 15m
144 g.region raster=ihs321_blue -p
145 d.rgb b=ihs321_blue g=ihs321_green r=ihs321_red
146 Results:
148 R, G, B composite of Landsat at 30m R, G, B composite of Brovey sharpened image at 15m
150 R, G, B composite of IHS sharpened image at 15m R, G, B composite of PCA sharpened image at 15m"
152 Pan sharpening of LANDSAT ETM+ (Landsat 7)
155 LANDSAT ETM+ (Landsat 7), North Carolina sample dataset:
156 # original at 28m
157 g.region raster=lsat7_2002_10 -p
158 d.mon wx0
159 d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30
160 # i.pansharpen with IHS algorithm
161 i.pansharpen red=lsat7_2002_30@PERMANENT \
162 green=lsat7_2002_20 blue=lsat7_2002_10 \
163 pan=lsat7_2002_80 method=ihs \
164 output=lsat7_2002_ihs
165 # display at 14.25m
166 g.region raster=lsat7_2002_ihs_red -p
167 d.erase
168 d.rgb r=lsat7_2002_ihs_red g=lsat7_2002_ihs_green b=lsat7_2002_ihs_blue
169 # compare before/after (RGB support in "Advanced"):
170 g.gui.mapswipe
171 # optionally color balancing:
172 i.colors.enhance r=lsat7_2002_ihs_red g=lsat7_2002_ihs_green b=lsat7_2002_ihs_blue
173
175 i.his.rgb, i.rgb.his, i.pca, d.rgb, r.composite
176
178 · Original Brovey formula reference unknown, probably...
179 Roller, N.E.G. and Cox, S., (1980). Comparison of Landsat MSS
180 and merged MSS/RBV data for analysis of natural vegetation.
181 Proc. of the 14th International Symposium on Remote Sensing of
182 Environment, San Jose, Costa Rica, 23-30 April, pp. 1001-1007
183 · Amarsaikhan, D., Douglas, T. (2004). Data fusion and multi‐
185 source image classification. International Journal of Remote
186 Sensing, 25(17), 3529-3539.
187 · Behnia, P. (2005). Comparison between four methods for data
189 fusion of ETM+ multispectral and pan images. Geo-spatial Infor‐
190 mation Science, 8(2), 98-103.
191 · Du, Q., Younan, N. H., King, R., Shah, V. P. (2007). On the
193 Performance Evaluation of Pan-Sharpening Techniques. Geoscience
194 and Remote Sensing Letters, IEEE, 4(4), 518-522.
195 · Karathanassi, V., Kolokousis, P., Ioannidou, S. (2007). A com‐
197 parison study on fusion methods using evaluation indicators.
198 International Journal of Remote Sensing, 28(10), 2309-2341.
199 · Neteler, M, D. Grasso, I. Michelazzi, L. Miori, S. Merler, and
201 C. Furlanello (2005). An integrated toolbox for image regis‐
202 tration, fusion and classification. International Journal of
203 Geoinformatics, 1(1):51-61 (PDF)
204 · Pohl, C, and J.L van Genderen (1998). Multisensor image fusion
206 in remote sensing: concepts, methods and application. Int. J.
207 of Rem. Sens., 19, 823-854.
208
210 Michael Barton (Arizona State University, USA)
211 with contributions from Markus Neteler (ITC-irst, Italy); Glynn
212 Clements; Luca Delucchi (Fondazione E. Mach, Italy); Markus Metz; and
213 Hamish Bowman.
214 Last changed: $Date: 2018-03-02 23:10:41 +0100 (Fri, 02 Mar 2018) $
216
218 Available at: i.pansharpen source code (history)
219 Main index | Imagery index | Topics index | Keywords index | Graphical
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222 © 2003-2019 GRASS Development Team, GRASS GIS 7.6.0 Reference Manual
224GRASS 7.6.0 i.pansharpen(1)