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Title
Technical Commission VII


n), (b)
correlation
to TIN, R?
ctral Index
(1)
band and
Our results
3and 4 was
NDSI has
is the input


3.0 Fusion of RADARSAT-2 image and HJ-1 CCD image
In order to accurately estimate the TIN in continuous sea
surface on a large scale, the fusion of RADARSAT-2 image and
HJ-1 CCD image was adopted. In this study, the RADARSAT-2
image in the study area was converted into 30 x 30 m grids by
nearest neighbor algorithm to keep consistency with HJ-1 CCD
image in the spatial resolution. The operation was conducted
using a routine written in MATLAB in combination with
ENVI 4.8. Two types of image data sources were corrected to
WGS-84 (World Geodetic System 1984) Geographic datum
and Geographic Lat/Lon projection system.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
3.3 Spatial distribution of input variables from image
Sea surface backscattering coefficients were converted to 30 x
30 m grids for RADARSAT-2 images. Figure 3 shows the
distribution VH, HV, Band4 and NDSI input variables in the
same spatial resolution. From Figure3, the backscattering
coefficient of HV ranged from —45 to 9 dB, whereas VH
ranged from —44 to 9 dB. HV and VH had the similar range,
and the former had a little higher range than the latter. Band4
reflectance had a low value with ranging from 0 to 0.012, while
NDSI ranged from -0.8 to 0.4. Detail statistics of four input
variables were calculated in Table 2.

(b)

0. 012





Figure 3. Distribution of four input variables in the same spatial resolution
(a) HV, (b) VH, (c) Band4, (d) NDSI








Input variables Min Max Mean Stdev
HV -44.08 7.98 -32.62 4.72
VH -43.86 8.07 -32.53 4.71
Band4 0 0.012 0.0011 0.0008
NDSI -0.79 0.36 -0.66 0.092




Table 2. Statistics of input variables in study area
3.4 Establishment of model
À series of HJ-1 CCD reflectance and SAR backscattering
coefficient were chose in the study area, in addition, they kept
the same geographical coordinates of situ measured TIN data.
The statistical model was created to establish the relationship
between the above four input parameters and TIN. Of all
statistical models, multiple regression analyses method was
most popular (Singh et al., 2009). Regression analysis is widely
used for predicting and forecasting, and it is also used to
understand which among the independent variables are related
to the dependent variable. Therefore, in this study, multiple
regression analysis was used to establish the relationship
between four input parameters and TIN.
3.5 Evaluation of model's performance