A Modified Watershed Technique for Segmentation of
High Resolution Satellite Images
2. METHODOLOGY
In this study, QuickBird multi-spectral and panchromatic images are considered as input dataset.
For extracting the most detail information from this dataset, the multi-spectral and panchromatic
modes of images are fused together for segmentation. The algorithm used here for images fusion
is based on the Brovey transformation method (Roller, 1980 and Hallada, 1983). In fact, Brovey
transformation only multiplies normalized multi-spectral image and original panchromatic
image to generate fusion result. Although Brovey transformation may produce spectral
distortion in the result (Hill, 1999), this method in deed can preserve the most original
information of input data without any nonlinear modification. However, for keeping the mean of
fused image the same as original multi-spectral image, a modified Brovey transformation
method is used. The modified algorithm is as follows:
F(n) = M(n) x P / P
mean n = 1..4 (1)
Where
P is the panchromatic image.
M(n) is the n-th band of multi-spectral image.
F(n) is the n-th band of fused image.
P
mean is the mean value of panchromatic image.
The following is the segmentation procedure for fused image. Firstly, we apply Sobel operators
to input images in column and row directions to create two edge images of fused image.
Secondly, the square root of the sum of squares taken from the two edge images in previous step
is used to measure edge intensity of source data. Thirdly, based on the watershed technique of
morphological theory, the concave regions in the image of edge intensity can be detected and
result an initial segmentation. Fourthly, an iterative patch merging procedure by various
threshold of mean difference is applied to the result of watershed to create multiple
segmentation layers. In addition, for each segmentation layer, another iterative processing is
needed for merging neighboring patches to ensure that the all mean differences between
neighboring patches are greater than a certain threshold.
Therefore, after the processing of iterative patch merging procedure, the spectral variation of
patches in each segmentation layer is different. The spectral variation of patches becomes
coarser as the region mean difference increase in each layer. This implies that the intensity of
the patch edges should be larger in coarser layer. Hence, the intensity of the patch edges can be
easily calculated by counting edge numbers for each pixel across all bands and all layers.
The last step is a filtering procedure over the intensity of the patch edges. There are two
criterions should be satisfied for a valid edge. First, the intensity of any point along an edge
should be equal to the maximum intensity in a local window with certain size. Second, the
intensity of any point along an edge should be larger than the mean of intensity of all patch
edges. The two criterions are used for local and global filtering respectively. Figure 1 is the flow
chart of proposed scheme.
Figure 1. The flow chart of proposed scheme.
