1i.pansharpen(1)             GRASS GIS User's Manual            i.pansharpen(1)
2
3
4

NAME

6       i.pansharpen   -  Image fusion algorithms to sharpen multispectral with
7       high-res panchromatic channels
8

KEYWORDS

10       imagery, fusion, sharpen, Brovey, IHS, HIS, PCA
11

SYNOPSIS

13       i.pansharpen
14       i.pansharpen --help
15       i.pansharpen [-slr] red=name green=name blue=name pan=name output=base‐
16       name  method=string  bitdepth=integer  [--overwrite]  [--help]  [--ver‐
17       bose]  [--quiet]  [--ui]
18
19   Flags:
20       -s
21           Serial processing rather than parallel processing
22
23       -l
24           Rebalance blue channel for LANDSAT
25
26       -r
27           Rescale (stretch) the range of pixel values in each channel to  the
28           entire 0-255 8-bit range for processing (see notes)
29
30       --overwrite
31           Allow output files to overwrite existing files
32
33       --help
34           Print usage summary
35
36       --verbose
37           Verbose module output
38
39       --quiet
40           Quiet module output
41
42       --ui
43           Force launching GUI dialog
44
45   Parameters:
46       red=name [required]
47           Name of raster map to be used for <red>
48
49       green=name [required]
50           Name of raster map to be used for <green>
51
52       blue=name [required]
53           Name of raster map to be used for <blue>
54
55       pan=name [required]
56           Name  of  raster  map  to  be used for high resolution panchromatic
57           channel
58
59       output=basename [required]
60           Name for output basename raster map(s)
61
62       method=string [required]
63           Method for pan sharpening
64           Options: brovey, ihs, pca
65           Default: ihs
66
67       bitdepth=integer [required]
68           Bit depth of image (must be in range of 2-30)
69           Options: 2-32
70           Default: 8
71

DESCRIPTION

73       i.pansharpen uses a high resolution panchromatic band from a multispec‐
74       tral  image to sharpen 3 lower resolution bands. The 3 lower resolution
75       bands can then be combined into an RGB color image at  a  higher  (more
76       detailed)  resolution  than is possible using the original 3 bands. For
77       example, Landsat ETM has low resolution  spectral  bands  1  (blue),  2
78       (green),  3 (red), 4 (near IR), 5 (mid-IR), and 7 (mid-IR) at 30m reso‐
79       lution, and a high resolution panchromatic band 8  at  15m  resolution.
80       Pan sharpening allows bands 3-2-1 (or other combinations of 30m resolu‐
81       tion bands like 4-3-2 or 5-4-2) to be combined into  a  15m  resolution
82       color image.
83       i.pansharpen  offers a choice of three different ’pan sharpening’ algo‐
84       rithms: IHS, Brovey, and PCA.
85       For IHS pan sharpening, the original 3 lower resolution bands, selected
86       as  red,  green  and blue channels for creating an RGB composite image,
87       are transformed into IHS (intensity, hue, and saturation) color  space.
88       The  panchromatic  band  is  then substituted for the intensity channel
89       (I), combined with the original hue (H) and  saturation  (S)  channels,
90       and transformed back to RGB color space at the higher resolution of the
91       panchromatic band. The algorithm for this can be represented as: RGB ->
92       IHS -> [pan]HS -> RGB.
93       With  a Brovey pan sharpening, each of the 3 lower resolution bands and
94       panchromatic band are combined using the following algorithm to  calcu‐
95       late 3 new bands at the higher resolution (example for band 1):
96                                band1
97           new band1 = ----------------------- * panband
98                        band1 + band2 + band3
99       In  PCA  pan sharpening, a principal component analysis is performed on
100       the original 3 lower resolution bands to create 3  principal  component
101       images (PC1, PC2, and PC3) and their associated eigenvectors (EV), such
102       that:
103            band1  band2  band3
104       PC1: EV1-1  EV1-2  EV1-3
105       PC2: EV2-1  EV2-2  EV2-3
106       PC3: EV3-1  EV3-2  EV3-3
107       and
108       PC1 = EV1-1 * band1 + EV1-2 * band2 + EV1-3 * band3 - mean(bands 1,2,3)
109       An inverse PCA is then performed, substituting  the  panchromatic  band
110       for PC1.  To do this, the eigenvectors matrix is inverted (in this case
111       transposed), the PC images are multiplied by the eigenvectors with  the
112       panchromatic  band  substituted for PC1, and mean of each band is added
113       to each transformed image band using the following  algorithm  (example
114       for band 1):
115       band1 = pan * EV1-1 + PC2 * EV1-2 + PC3 * EV1-3 + mean(band1)
116       The  assignment  of  the channels depends on the satellite. Examples of
117       satellite imagery with high resolution panchromatic  bands,  and  lower
118       resolution spectral bands include Landsat 7 ETM, QuickBird, and SPOT.
119

NOTES

121       The module works for 2-bit to 30-bit images. All images are rescaled to
122       8-bit for processing. By default, the entire possible range for the se‐
123       lected  bit  depth  is  rescaled  to  8-bit.  For example, the range of
124       0-65535 for a 16-bit image is rescaled to 0-255). The ’r’  flag  allows
125       the  range  of  pixel values actually present in an image rescaled to a
126       full 8-bit range. For example, a 16 bit image might  only  have  pixels
127       that  range  from 70 to 35000; this range of 70-35000 would be rescaled
128       to 0-255. This can give better visual distinction  to  features,  espe‐
129       cially when the range of actual values in an image only occupies a rel‐
130       atively limited portion of the possible range.
131       i.pansharpen temporarily changes the computational region to  the  high
132       resolution  of  the  panchromatic  band during sharpening calculations,
133       then restores the previous region settings. The current region  coordi‐
134       nates (and null values) are respected. The high resolution panchromatic
135       image is histogram matched to the band it is replaces prior to  substi‐
136       tution  (i.e.,  the  intensity  channel for IHS sharpening, the low res
137       band selected for each color channel with Brovey  sharpening,  and  the
138       PC1 image for PCA sharpening).
139       By default, the command will attempt to employ parallel processing, us‐
140       ing up to 3 cores simultaneously. The -s  flag  will  disable  parallel
141       processing,  but  does  use an optimized r.mapcalc expression to reduce
142       disk I/O.
143       The three pan-sharpened output channels may be combined with  d.rgb  or
144       r.composite.  Colors may be optionally optimized with i.colors.enhance.
145       While the resulting color image will be at the higher resolution in all
146       cases,  the 3 pan sharpening algorithms differ in terms of spectral re‐
147       sponse.
148

EXAMPLES

150   Pan sharpening of LANDSAT ETM+ (Landsat 7)
151       LANDSAT ETM+ (Landsat 7), North Carolina sample dataset, PCA method:
152       # original at 28m
153       g.region raster=lsat7_2002_10 -p
154       d.mon wx0
155       d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30
156       # i.pansharpen with PCA algorithm
157       i.pansharpen red=lsat7_2002_30 \
158         green=lsat7_2002_20 blue=lsat7_2002_10 \
159         pan=lsat7_2002_80 method=pca \
160         output=lsat7_2002_15m_pca -l
161       # color enhance
162       i.colors.enhance blue=lsat7_2002_15m_pca_blue \
163         green=lsat7_2002_15m_pca_green red=lsat7_2002_15m_pca_red
164       # display at 14.25m, IHS pansharpened
165       g.region raster=lsat7_2002_15m_pca_red -p
166       d.erase
167       d.rgb b=lsat7_2002_15m_pca_blue g=lsat7_2002_15m_pca_green r=lsat7_2002_15m_pca_red
168
169       LANDSAT ETM+ (Landsat 7), North Carolina sample dataset, IHS method:
170       # original at 28m
171       g.region raster=lsat7_2002_10 -p
172       d.mon wx0
173       d.rgb b=lsat7_2002_10 g=lsat7_2002_20 r=lsat7_2002_30
174       # i.pansharpen with IHS algorithm
175       i.pansharpen red=lsat7_2002_30 \
176         green=lsat7_2002_20 blue=lsat7_2002_10 \
177         pan=lsat7_2002_80 method=ihs \
178         output=lsat7_2002_15m_ihs -l
179       # color enhance
180       i.colors.enhance blue=lsat7_2002_15m_ihs_blue \
181         green=lsat7_2002_15m_ihs_green red=lsat7_2002_15m_ihs_red
182       # display at 14.25m, IHS pansharpened
183       g.region raster=lsat7_2002_15m_ihs_red -p
184       d.erase
185       d.rgb b=lsat7_2002_15m_ihs_blue g=lsat7_2002_15m_ihs_green r=lsat7_2002_15m_ihs_red
186       # compare before/after (RGB support under "Advanced"):
187       g.gui.mapswipe
188
189   Pan sharpening comparison example
190       Pan sharpening of a Landsat image from Boulder, Colorado, USA  (LANDSAT
191       ETM+ [Landsat 7] spectral bands 5,4,2, and pan band 8):
192       # R, G, B composite at 30m
193       g.region raster=p034r032_7dt20010924_z13_20 -p
194       d.rgb b=p034r032_7dt20010924_z13_20 g=lp034r032_7dt20010924_z13_40
195           r=p034r032_7dt20010924_z13_50
196       # i.pansharpen with IHS algorithm
197       i.pansharpen red=p034r032_7dt20010924_z13_50 green=p034r032_7dt20010924_z13_40
198           blue=p034r032_7dt20010924_z13_20 pan=p034r032_7dp20010924_z13_80
199           output=ihs321 method=ihs
200       # ... likewise with method=brovey and method=pca
201       # display at 15m
202       g.region raster=ihs542_blue -p
203       d.rgb b=ihs542_blue g=ihs542_green r=ihs542_red
204
205       Results:
206
207         R, G, B composite of Landsat at 30m                         R, G, B composite of Brovey sharpened image at 15m
208
209         R, G, B composite of IHS sharpened image at 15m             R, G, B composite of PCA sharpened image at 15m"
210
211

REFERENCES

213           •   Original Brovey formula reference unknown, probably...
214               Roller,  N.E.G.  and Cox, S., (1980). Comparison of Landsat MSS
215               and merged MSS/RBV data for  analysis  of  natural  vegetation.
216               Proc.  of the 14th International Symposium on Remote Sensing of
217               Environment, San Jose, Costa Rica, 23-30 April, pp. 1001-1007
218
219           •   Amarsaikhan, D., Douglas, T. (2004).  Data  fusion  and  multi‐
220               source  image  classification.  International Journal of Remote
221               Sensing, 25(17), 3529-3539.
222
223           •   Behnia, P. (2005). Comparison between four methods for data fu‐
224               sion of ETM+ multispectral and pan images. Geo-spatial Informa‐
225               tion Science, 8(2), 98-103.
226
227           •   Du, Q., Younan, N. H., King, R., Shah, V.  P.  (2007).  On  the
228               Performance Evaluation of Pan-Sharpening Techniques. Geoscience
229               and Remote Sensing Letters, IEEE, 4(4), 518-522.
230
231           •   Karathanassi, V., Kolokousis, P., Ioannidou, S. (2007). A  com‐
232               parison  study  on  fusion methods using evaluation indicators.
233               International Journal of Remote Sensing, 28(10), 2309-2341.
234
235           •   Neteler, M, D. Grasso, I. Michelazzi, L. Miori, S. Merler,  and
236               C.   Furlanello  (2005). An integrated toolbox for image regis‐
237               tration, fusion and classification.  International  Journal  of
238               Geoinformatics, 1(1):51-61 (PDF)
239
240           •   Pohl,  C, and J.L van Genderen (1998). Multisensor image fusion
241               in remote sensing: concepts, methods and application.  Int.  J.
242               of Rem. Sens., 19, 823-854.
243

SEE ALSO

245        i.his.rgb, i.rgb.his, i.pca, d.rgb, r.composite
246

AUTHORS

248       Michael Barton (Arizona State University, USA)
249       with   contributions  from  Markus  Neteler  (ITC-irst,  Italy);  Glynn
250       Clements; Luca Delucchi (Fondazione E. Mach, Italy); Markus  Metz;  and
251       Hamish Bowman.
252

SOURCE CODE

254       Available at: i.pansharpen source code (history)
255
256       Accessed: Saturday Oct 28 18:19:19 2023
257
258       Main  index | Imagery index | Topics index | Keywords index | Graphical
259       index | Full index
260
261       © 2003-2023 GRASS Development Team, GRASS GIS 8.3.1 Reference Manual
262
263
264
265GRASS 8.3.1                                                    i.pansharpen(1)
Impressum