all the reference data were selected randomly and 
independently from the training/test data used in building ISP 
prediction model above. 
In addition, the ISP map produced from the remote sensing 
dataset in this study belongs to continuous data. Therefore, the 
accuracy assessment method based on error matrix and kappa 
index used often in classification cannot be appropriate here 
because it is designed for categorical data only. In this study, 
correlation analysis was used for the quantitative comparison 
between the estimated ISP and the reference data. Three 
indicators, Average Error (AE), Relative Error (RE) and 
Pearson coefficient (R2), were calculated to assess accuracy of 
the ISP estimation. 
4. EXPERIMENT RESULTS 
To test the effectiveness and feasibility of the synergistic use of 
optical and InSAR data in urban ISP mapping, three group of 
data sources used in this study were (1)4 SPOT HRG multi- 
spectral bands, here referred to SPOT 4, (2) 3 InSAR products 
(referred to InSAR_3), and (3) 4 SPOT bands plus 3 InSAR 
products (referred to SPOT_4+InSAR_3). 
estimate derived from the three groups of middle-resolution 
remote sensing dataset were illustrated in Figure6. They were 
post-processed by masking sea water boundary. Figure6 shows 
that The ISP of the urban area located in the commercial 
business district of Kowloon and Hong Kong Island was greater 
than 60%, and that of the suburbs and hill areas was less than 
40%. 
Visually, three ISP results shown in Figure 6 were quite 
reasonable in spatial distribution pattern and the density urban 
areas with high ISP value were similarly mapped. But the ISP 
estimate results using just InSAR products were fragmentized 
due to coarse spatial resolution and signal distortion in roll 
areas (Figure 5(a)). In addition, checking by the use of high- 
resolution CIR aerial photographs suggested that the SPOT- 
derived ISP was slightly overestimated in the low to middle ISP 
areas (e.g., bared soils or vegetated fields) as has been reported 
in the previous works (Figure5(b)). This ISP overestimation 
problem using SPOT data may be due to similar spectral 
reflectance between bared soils or spared vegetation and urban 
imperious surface such as parking plots. However, this 
overestimation was greatly reduced by synergistic use of SPOT 
ISP value 
Water 
0-30% 
30-46% 
46-63% 
63-78% 
78-91 % 
91-100% 
(a) 
(b) 
(c) 
Figure 5 ISP classification results derived from the three groups of remote sensing datasets in the study area (a) InSAR_3; 
(b) SPOT 4; (c) InSAR 3+SPOT 4 
The CART based ISP estimate approach was respectively 
applied to the three data sources and corresponding ISP 
prediction models were built. The classification results of ISP 
and InSAR dataset (Figure 5(c)). It should be attributed to the 
fact that InSAR features, in particular long-term interferometric 
coherence, possessed the capacity to distinguish the two land- 
cover classes mentioned above. It further and in detail was