The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
that the latter one overrates the size of regions and makes the 
segmentation results unstable. In the merging process, we 
utilize RAG (Region Adjacency Graph) to describe the blocks 
after splitting. The RAG consists of three components: V, E 
and M. V is a set of region nodes to record the region 
information. E is an adjacency matrix to record the pseudo 
address of each region edge. The M matrix records the merger 
importance values of all the pairs of adjacent regions of an 
image. At each merging step, we search the smallest MI of the 
M matrix and merge the pair of adjacent regions that has the 
smallest MI. Then we adjust the RAG, and merge the pair of 
adjacent regions that has the smallest MI in the changed M 
matrix. Merging proceeds until the following stopping criteria 
is true: 
MIR = CJ ^>Y 
Ml 
max . (12) 
Where MI cur and MI max denote the merger importance for the 
current best merge and the largest merger importance of all 
preceding mergers. Threshold Y is determined experimentally. 
A boundary refinement algorithm is used to refine the 
boundaries of the blocky segmented image. For an examined 
boundary point P, a discrete square with a dimension d around 
the pixel is placed and the MI between the square and the 
neighboring regions of point P is computed. The pixel is 
relabelled if the label of the neighboring region that has the 
smallest MI is different from the label of P. At the following 
step, we only consider the boundary points that have relabelled 
at the previous sweep. The procedure is iterative and proceeds 
until the un-relabelled number of the boundary pixels is less 
than 50 or the iteration times are larger than 30. 
4. EXPERIMENTS AND RESULTS 
The objective of the present experiments was to evaluate the 
effectiveness of the novel features of texture and spectral 
distributions and the very simple weight combination approach 
in segmentation of H-res remote sensing imagery. Besides, 
we discuss the effect of several parameters, i.e. weight 
determination, MI and thresholds, on the result of segmentation 
for the purpose of obtaining improved results and finding a way 
of solving SIMF better. 
The performance of the method was evaluated with 256x256 
pixel multi-spectral IKONOS-2 satellite images. IKONOS-2 
data contain red, green, blue and near-infrared (NIR) channels 
at 4.0 m spatial resolution. Since the colour images are the most 
common in application and can provide more information than 
grayscale images, the paper is endeavour to explore 
segmentation approach that make good use of multi-spectral or 
colour information. 
The experiments were performed using the following 
procedure. The original multi-spectral images are transformed 
by PCA. We just take the first two PCs for feature extraction. 
They collect more than 95% information of the original images. 
For texture features, we computed texture labelled images of 
the PCs by rotation invariant LBP form and we got two LBP 
labelled images which were used to obtain the discrete two 
dimensional texture histograms. The texture similarity of two 
regions was calculated by their two-dimensional texture 
histograms. The spectral histogram was gotten by their joint 
distribution of the gray-scale pixel values of the PCs. So we got 
the spectral similarity of two regions from their spectral 
histograms. The first PC was used to calculate the attribute of 
regions by their standard deviation, which was applied to 
weight determination of the two features. The weighted sum 
similarity measures were used to the whole coarse to fine 
segmentation process. 
Figure 2. Segmentation results of H-res images based on 
texture distributions calculated by LBP^\ and 
spectral distributions. 
Figure 2 shows the segmentation results by SIMF approach 
based on texture distributions calculated by LBP^\ and 
spectral distributions. The result demonstrates that the SIMF 
approach by our feature extraction method performs well on 
complex H-res satellite images. 
5. DISCUSSION AND CONCLUSION 
The paper presented a novel feature extraction method that 
considers the cross band relations and a new segmentation 
framework SIMF suitable for segmenting multi-spectral images. 
Figure 2 demonstrates the satisfied segmentation results. It 
shows thatLBP%\ is a robust LBP operator for texture feature 
extraction. Despite that, the feature weight determination is still 
a necessary research topic in the future since the images are 
very complex and different textures may be used to SIMF. 
Based on the previous experiments and results, we point out the 
future research works. The feature extraction method is very 
important for SIMF. The future research should concentrate on 
finding more appropriate features adaptive to different kinds of 
images, e.g. that of various resolution. The feature weight 
combination approach determines whether the combining 
features can discriminate heterogeneous regions to a large 
extent, which is still an open problem. The MI determines the 
sequence of merging of pairs of homogeneous regions and the 
stopping criterion for merging. MIR determines when to stop 
the merging process and the scale of segmentation results. So 
the future research should explore MIR that can implement 
multiscale segmentation. Similarity measure of feature