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Title
Mapping without the sun
Author
Zhang, Jixian

(a) image (b)Histogram thresholding
segmentation
(c)K-means segmentation (d)Multi-scale segmentation
on SOM
Figure 4.Point cloud image and segmentation
Fig.3(b) is a segmentation result of Fig.3(a) by histogram
threshold value algorithm. In Fig.3(b), the contour of road
network are clear, but the water area in the lower right comer
can not be partitioned. In Fig.3(c), the segmentation result show
that K-NN algorithm has some smooth effort of image noises
and the contour of road are also clear. By analyzing, we find
that using histogram threshold value algorithm and K-NN
algorithm is great effective for extracting geographic objects
with greater contrast of radiant intensity. Because of the greater
contrast between the road and the background in Fig.3, the
segmentation contour of road network are clear in Fig.3(b) and
Fig.3(c). In Fig.3(d), the image is segmented by using SOM
based on the directional wavelet transform at multiple-scale. By
comparing the different result, we find that SOM segmentation
prior mark winner of the competition, and the lateral inhibition
keep the nonlinear connection at the same time. According to
the effect of the segmentation contour of road, it is found that
the class flag is weakened gradually in the topologic
neighbourhood of victorious features. That is to say there still
are some activated features around the contour of road class. In
Fig.3(d), the water area in the lower right comer is segmented
equality ,clearly and accurate. It is indicated that the SOM
segmentation is obviously effective for extracting the block
objects.
Fig.4(b) is a segmentation result of Fig.4(a) by histogram
threshold value algorithm. It show that histogram threshold
value algorithm has good effect on segmenting images which
have greater contrast of intensity between objects and
background. Fig.4(c) is a segmentation result by K-NN
algorithm. It is observed that K-NN algorithm has some smooth
effort of image noises and the contour of ancient architectural
structures are clear. In Fig.4(d), the image is segmented by
using SOM based on the directional wavelet transform at
multiple-scale. According to foregoing analyzing, there is a
certain influence among the features of SOM segmentation and
activated features still exist in the topologic neighborhood of
victorious features. SOM segmentation is good for extracting
the block objects
5. CONCLUSION
There exist some problems in multi-scale wavelet transforms
based on Mallat fast transform of discrete wavelets, in which
image features may be only acquired in horizontal, vertical, and
diagonal directions, and in k-nearest neighbor (k-NN )
segmentation the relation between image features are not
considered. We propose a novel approach in which remote
images may be segmented in two steps based on the directional
wavelet transform at multiscale. Firstly, the images are
transformed by the directional wavelet at coarser scale, multi
dimension feature vectors are constructed, and the images are
coarsely segmented by k-NN algorithm. Secondly, the result of
coarse segmentation is used for prior messages , at fine scale
the image segmentation is performed again by self-organizing
map algorithm(SOM). In this paper, airspace and 3D-laser
cloud points image are segmented by histogram threshold value
algorithm, K-NN algorithm and our method. The result of
experiment show that our approach is obviously good for
extracting the block object. The disadvantages of SOM
segmentation is that the computation cost much time. The
reason is that it orderly update victorious features in every time
of iteration(serial algorithm), but K-NN update all in every time
of iteration(parallel algorithm ). At present, the improved
research about SOM parallel algorithm is being studied.
Because of extracting the geographic objects in remote sensing
images is a emphasis of the research about remote sensing
technology and application. The emphases in the farther study
are followed: algorithm and adaptability about different remote
sensing images and geographic objects, the image construe and
algorithm based on knowledge, algorithm about extracting
geographic objects based on multi-sensors remote sensing
image fusion, the image construe and algorithm about multi
band remote sensing image.
6. REFERENCES
6.1 References and/or Selected Bibliography
References from Journals:
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C.Fosgate, H.Krim, W.Iring, et al. Multiscale Image
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C T Li. Multiresolution Image Segmentation Integrating Gibbs
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