Fig 3: FCC of mean intensity (Red), intensity difference 
(Green) and coherence image (Blue) 
It is observed that the inclusion of coherence band along with 
the backscatter coefficients in HH polarization has enhanced 
the delineation capability compared to stand alone HH 
backscatter values. The discrimination of Forest, Agriculture, 
Water, barren/fallow and urban settlements is distinctly 
observed (Fig 4). Coherence is a function of systemic spatial 
decorrelation, the additive noise, and the scene decorrelation 
that takes place between the two acquisitions. It is observed 
from the coherence image, that the average value of coherence 
in the study area was very low (<0.5) as the majority area is 
covered by vegetation. This can be attributed to the wind 
patterns in the study area that might alter the orientation of 
scattering objects (leaves, secondary branches) in the vegetation 
layer. Highest coherence values were observed in barren areas 
followed by agricultural areas over the study areas, which are in 
accordance with the results reported in earlier studies 
(Wegmuller and Werner 1997), which suggested urban areas, 
agricultural areas, bushes and forests have different correlation 
characteristics, with urban areas showing the highest correlation 
and forest the lowest. Medium coherence and high backscatter 
difference values were observed for the agriculture areas. As 
the temporal gap between the two images acquired is 35 days, 
agriculture areas showed some difference during the two 
acquisitions. It is observed that both the coherence and 
backscatter difference values of the forested areas as low 
compared to other land cover types. This is attributed to the 
However, it was observed that in some areas the shadow areas 
were misclassified as water bodies and steep terrain areas with 
high backscatter values have some overlap with urban 
settlements in the False Colored Composite (FCC). Though 
there was a clear distinction between vegetation and non 
vegetation, the discrimination within the forest types was 
observed to be less clear in the False Colored Composite 
(FCC). So, an attempt has been made to discriminate different 
forest types by merging the optical LISS-III data with HH 
polarized ASAR data. 
5.2 Multi-sensor Fusion analysis: 
The merged output has been found to better delineate the forest 
types (Fig 6) apart from other land-cover classes and minimize 
the shadow effect. The IHS refers to the transformation of three 
image channels assigned to I, H and S (Rast et al. 1991). The 
second transforms three channels of the data set representing 
RGB into the IHS colour space which separates the colour 
aspects in its average brightness (intensity). This corresponds to 
the surface roughness, its dominant wavelength contribution 
(hue) and its purity (saturation) (Carper et al. 1990). Both the 
hue and the saturation in this case are related to the surface 
reflectivity or composition. Then, one of the components is 
replaced by a fourth image channel which is to be integrated. 
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