Figure. 1.The colors ynthetized map of three polarizations basis (HH, HV, VV) of the LingShui area. The three test areas represented 
as red, blue and fuchsine color rectangles respectively. 
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 
    
   
  
bere 
surface 
  
crop forest building areca 
lychee paper — mango 
Figure.2.Ground measurements superimposed on the polarimetric color synthetized map. 
During the period of data acquisition, the associated ground 
survey campaign was also implemented in test area, composed 
of bare surface, orchards, cropland, forests, and residential area, 
as shown in figure.2. Figure.1. presents the study area as a color 
composite where the backscattered powers corresponding to the 
three standard basis’ polarizations (HH, HV, VV) are 
respectively coded in red, green, and blue. Orchards and forests 
appear in green color, buildings in red color, and cropland in 
blue or green color. The typical test areas marked with 
rectangles are selected as experimental areas of land 
classification, man-made objects detection and DSM mapping 
shown in figure. 2. The test areal in red color is selected as land 
classification experiment, test area 2 in fuchsine color is 
selected as man-made objects detection experiment, test area3 
in blue color is selected as DSM mapping experiment. 
3. POLARIMETRIC ANALYSIS 
3.1 Polarimetric Parameters 
The classical polarimetric parameters, the entropy, the alpha 
angle, and the anisotropy describe the polarimetric properties of 
different surfaces which have been proposed in (S.R.Cloude, 
1997). As the alternative parameters, surface scattering fraction, 
scattering diversity and etc. can be used by a fairly simple 
algorithm (E.Colin, 2010). The alternative parameters will 
provide the easier way of image interpretation, classification 
and visualization. Therefore, as the new adjustment for 
polarimetric parameters, the alpha angle is replaced by surface 
scattering fraction, entropy is adjusted to the simpler formula 
avoiding any eigenvalue calculation(J.Praks, 2009), anisotropy 
retains the classical formula, scattering diversity is appended as 
the new polarimetric parameter. 
3.2 Polarimetric Analysis for Datasets 
Surface scattering fraction, entropy, anisotropy and scattering 
diversity of datasets are firstly computed over a 9 X 9 sliding 
window. To proceed to a more quantitative analysis of 
polarimetric parameters, some representative areas are analyzed 
in surface scattering fraction /entropy, the surface scattering 
fraction /anisotropy and surface scattering fraction /scattering 
diversity planes with a 30 X30 pixel window. Entropy-Surface 
fraction scatter plots presented in figure.3 show that surface 
scattering fraction can discriminate between bare surface, forest, 
building and cultivated lands (crops and orchards). However, 
for different types orchards and crops, surface scattering 
fraction can not discriminate them. Entropy represents the best 
discrimination capability, as shown in figure.3, except for the 
same capability as surface scattering fraction has, it still can 
identify areca from other cultivated lands since it behaviours 
similar property of higher entropy with forest covered with 
more leaves. Figure.4. show that scattering diversity gives the 
results close to the entropy, but it represents the lower 
discrimination capability than Entropy. Figure.5. show that 
anisotropy can identify buildings from the other types well as 
the observations of eigenvalues are different. Therefore, surface 
scattering fraction, entropy, anisotropy are selected for land 
classification and objects detection as the following experiments. 
  
    
  
  
  
08 
gue 
> 067 E 
8 ® bung 
= 044 i 
ud ^ 
02 + 
0 i 1 i : "m 
15 25 35 45 55 65 75 
Surface scattering fraction 
Figure.3.Entropy-Surface fraction scatter plot of different types 
of lands