8, 2012
n the Landsat
Spot 5 scene
as input data.
m full scenes
and bias for
| the metadata
Landsat 5 TM
Landsat 5 TM
nost the same
XG sensor
Sensitivity
(pm)
N/A
0,50 - 0,59
0,61 - 0,68
0,79 - 0,89
1,58 - 1,75
N/A
N/A
[ and SPOT 5
1 Spot 5 HRG
is red- SWIR,
ivities of both
in practice we
e do not see
| image show
f construction
righer mixture
on 022222 of
face, irrigated
Landsat 5 TM
from green to
te different in
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
implementation. These differences are caused maybe by
different spatial resolution, sensor construction or viewing angle
of the sensor during observation. While water body is captured
in Landsat 5 TM only in modulation 222222 and 022222, for
Spot 5 HRG, water is located in 4 modulations: 222222,
022222, 002222 and 002022.
Figure 3. Water by modulation 222222 for Landsat 5 TM (left)
and Spot 5 (right)
5 (right)
Heavily turbid
water
cus (E
Shallow water
rice field
Figure 5. Modulation 00222 (left) and 002022 (right) for Spot 5
For Spot 5 HRG clear water is captured mainly in modulation
222222 and 022222 and turbid water has reflectance curve
modulation as 002222 and 002022. In modulation 002022 there
are heavily turbid water and very shallow water rice field
(Figure 5).
2.3 Water Body Extraction Algorithm
For new comers in spectral pattern analysis there is almost
unknown knowledge about how many spectral patterns for the
given image could be and what ground objects a particular
spectral pattern stand for. The author developed an useful utility
to decompose the given image into sub-images containing only
pixels of single spectral pattern. Statistics on how many spectral
patterns the given images can be decomposed and number of
pixels for each spectral pattern are given in Table 2. Blue colour
filled rows indicate spectral patterns for water bodies.
Landsat 5 TM image Spot 5 HRG image
Spectral Number of Spectral Number of
tt ixels attern ixels
202022 787863 202022 394708
002022 239842
002222 90760
222022 42689 222022 4597
Table. 2 Statistics on spectral patterns and number of pixels for
each spectral pattern for Landsat 5 TM and Spot 5 HRG image
of study area. Colour shaded rows indicate spectral patterns for
water.
Some sub images with single spectral pattern have been showed
in Figure 2, 3, 4 and 5. After confirmation which spectral
pattern contains water surface the analysis was implemented by
the following algorithm. The water body extraction is consisted
of 4 steps for Landsat TM and 6 steps for Spot 5 HRG:
a. Reading image data and finding out gain and bias
coefficients for conversion from DN to reflectance.
b. Conversion from DN to reflectance and determining
modulation of spectral reflectance curve for each
pixel vector
c. Case 222222: applying different threshold values for
SWIR band of TM and HRG sensors to extract water.
d. Case 022222: applying different threshold values for
SWNIR band of TM and HRG sensors to extract
water.
e. Case 002222 — Spot 5 only: applying threshold value
for SWNIR band to extract water.
f. Case 002022 — Spot 5 only: applying threshold value
for SWIR band to extract water.
It is obvious that the algorithm is composed of two parts:
decomposition of the image into sub images according to
spectral reflectance patterns and level slicing into water and
land using different threshold values for the SWIR band. If we
apply a single threshold value for water extraction there might
be under or over estimation in clear and turbid water area
because reflectance of turbid water is always higher than clear
water. Figure 7 explains water body under estimation by level
slicing of Spot SWIR band.
Under estimation of
water
Figure 6. Spot 5 colour composite image (left) and water
extraction by level slicing of SWIR band (right)
