The International Archives of the Photogrammetry, Remote Sensing and Spatial information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
a. b. c. 
Figure 3 K-means segmentation results of two 
images. The first column uses Lab color, 
the second use all Gabor PCs features; 
the third uses a part of PCs which is 
determined by contribution threshold 
0.98(described in section 2.1) 
From the first column, we can find the spectral information 
performed well when the great spectral difference exists in 
between classes and the result has good discontinuity 
preserving performance. However, the grass and tree in the first 
image, farm land and tree are mixed due to their similarity in 
spectral information. On the contrary, Gabor feature provide 
better regional information, but weak boundary. In addition, by 
comparing the second and the third column, it is obviously that 
Figure 4. The k-means results using combinations of 
two type of feature with different scales. 
The first column, feature is combined 
without any scale change. The second 
column, Lab feature is scaled by 
multiplying a constant 4/125; and Gabor 
PCs is mapped linearly into range [0 1]. 
4.4 Experiment 3 
In this part, our algorithm compared with a fixed bandwidth 
mean shift segmentation in (Comaniciu and Meer, 2002) with 
default parameters.. 
the performance of Gabor feature doesn’t reduce significantly 
with the reduction of number of Principle Components in some 
degree. From the result, we can conclude the Lab color and 
Gabor feature may provide us some complementary information 
about the HR imagery. In the next section the potential of 
spectral and spatial feature fusion will be evaluated. 
4.3 Experiment 2 
In this part, the combination of spatial and spectral information 
with different scales is compared (as shown in Figure 4). 
From the first column, we can find that spectral feature seems 
to play a dominant role in segmentation since the tree and grass 
pair in the upper image, farm land and tree pair in the lower are 
similar in the spectral surface and classes mixed together. 
Fundamentally, it caused by the different nature of the features, 
which are measured in different measurement units and 
occupying quite disparate value ranges. In order to get a 
balanced contribution of all features to classification and 
preserve semantic information, a simple scale factor is chosen. 
The segmentation result with standardized data is shown in the 
second column. Compared with the first, the confusion of 
different land cover classes is reduced, especially for the upper 
one. 
In addition, the different classes are labeled randomly since we 
use K-means for final result; it means that even the same land- 
cover types in different result images must be labeled 
differently. So we judge the performance of results by 
comparing with the original image directly. 
a. synthetics texture 1 b. synthetics texture2 
Figure 2. Two texture images synthesized from the same 
QucikBird image of Wuhan, China. And each 
raster band has been qualified into 256 gray 
levels. The size of the first one is 128 X 
128.the second is 256X256. 
4.2 Experiment 1 
To validate the differentiate power of features used in the 
algorithm; the performance of individual spectral features, all 
Gabor PCs features and the retained PCs are represented. In 
those experiments, each feature set is put into K-means 
classifier directly; classification results for both images are 
shown in the Figure 3. 
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