International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
2.1.1 Hue-Saturation-Value (HSV): HSV is one of the
most often used methods to merge multisensor image data,
and has been widely used to merge Landsat TM and SPOT-P
data (Chavez et al., 1991). The method uses three bands of
the lower spatial resolution image and transforms these data
to the HSV space. The higher spatial resolution image is
constantly stretched in order to adjust the mean and variance
to unit intensity. The stretched image replaces the intensity
component image before the images are back-transformed to
the RGB space.
2.1.2 Colour normalized (CN): The colour normalized
method (Vrabel, 1996) uses a mathematical combination of
the colour image and high-resolution data to merge the
higher spatial and higher spectral resolution images. Each
band in the higher spectral image is multiplied by a ratio of
the higher resolution data divided by the sum of the colour
bands. The function automatically resamples the three-colour
bands to the high-resolution pixel size using a nearest
neighbour, bilinear, or cubic convolution technique. The
output RGB images will have the pixel size of the input high-
resolution data.
2.2 Geostatistical Simulation
The basic objective of this procedure is the application of
geostatistical simulation techniques (direct sequential
cosimulation, Soares, 2001) to obtain simulated values of the
10m Landsat TM image from the original 30m Landsat TM
values and the existing correlation between the Landsat TM
and SPOT P images. Here, an additional condition applies:
the mean value of the 9 pixels of the 10m cosimulated
Landsat TM-SPOT P image (3x3 pixels) must be equal to the
30m Landsat TM original values.
From a quantitative point of view, we intend with the
simulation process to obtain a simulated image that
reproduces the statistical characteristics. of the merged
images. The simulated image must have the same mean value
as the 30m Landsat TM image and the same variance and
variogram as the SPOT PAN image.
The core of the proposed merging procedure lies in the use of
geostatistical simulation techniques. These techniques allow
generating several realizations of the original values with a
specific pixel size, preserving the basic statistical
characteristic of the original images and using information
derived from the high-resolution image according to the level
of correlation.
Let TM;(x) be the digital value of the original 30m Landsat
TM image for the band i in the pixel of position x, PAN(x)
the value of the original 10m SPOT-PAN image in the same
position and finally, TMsim(x) the digital value of the
simulated 10m Landsat TM-SPOT PAN image in the
position x. The simulated TMsim(x) must satisfy the
following requisites:
I. For any digital value ND: prob{TM(x)<NDj}=
=prob{TMsim(x)<ND};
2. Ypan(h)FYrmsim(h), Where Ypan(h) and Yrmsim(h) are
the variograms of the original SPOT-PAN and simulated
Landsat TM-SPOT PAN merged image, respectively;
3. Conditioning of the simulated images to the
following condition: the mean of the pixels grouped
according to the 3x3 pixels scheme must be equal to the
30m Landsat TM original image values.
896
The method used for simulation was the Direct Sequential
Cosimulation procedure (Co-DSS) (Soares, 2001). One of the
main advantages of this algorithm over traditional simulation
methods is that it allows a joint simulation dealing directly
with the original images.
The DSS algorithm is applied to simulated TM(x) in a 10m
grid using TM(x) as primary information and PAN(x) and the
local correlation coefficient as secondary information and
using the Markov-type approximation of the collocated
cokriging method according Goovaerts (1997).
2.3 Geostatistical merging procedure
The geostatistical image merging method can be summarized
in the following steps (Figure 2):
1. Calculation of the basic statistics, correlation matrix and
variograms of the several images (bands) that take part in the
merging process. The calculation is applied to the Landsat
TM bands and SPOT-P image.
2. For each band:
a) Generation of a sufficiently high number of
cosimulated images. These images are generated using
the direct cosimulation method utilizing as primary
information each of the Landsat image's bands, the high-
resolution image (SPOT-PAN image) as secondary
information and the local correlation coefficient
between Landsat TM and SPOT-P (defined in a
150x150 m window). A total of 10.000 simulated
images with 10 m pixel size that integrate Landsat and
SPOT-PAN information was generated for each band;
b) Resampling the simulated images, grouping them
in 30x30m size (3x3 pixels) and obtaining 30m
simulated Landsat-SPOT images;
c) For each pixel, comparison of the 30m Landsat TM
original values and 30m resampled simulated Landsat-
SPOT images. Three cases are possible:
1. There is only one image where the resampled
simulated images are equal to the original image.
In that case, these simulated pixels are selected as
definite in the final image.
2. There are several pixels (different simulated
images) that meet the previous condition. In that
case, the simulated image that presents the
maximum local correlation (defined in a 30 x 30m
window) between SPOT-P and Landsat-TM is
selected.
3. There is any pixel that verifies the condition. In
this case, it is necessary to obtain additional images
using the procedure pointed out in step 2.
3. When all of the pixels are obtained, a final checking
process is carried out. The objective of this process is 10
locate all the pixels that present problems in the simulation.
These pixels are usually pixels in which the local correlation
values and SPOT-P values are very restrictive. In this cast,
erratic values are obtained. These values can be adjusted
using a proportional coefficient that adjust the 30m
resampled simulated mean values to the corresponding
Landsat image values.
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