Full text: Mapping without the sun

88 
REMOTE SENSING IMAGE SEGMENTATION 
BASED SELF-ORGANIZING MAP AT MULTI-SCALE 
Zhao Xi-an, Zhang Xue-wen Wei Shi-yan a 
Dep. of Surveying and Mapping and Urban Spatial Information, Beijing Institute of Civil Engineering and 
Architecture, Beijing China, zhaoxian@bucea.edu.cn 
Commission VI, WG VI/4 
KEY WORDS: Directional Wavelets, Multi-dimension Feature Vectors, SOM Image Segmentation 
ABSTRACT: 
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 multi-scale. 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) . Finally the paper approach has been compared with the histogram threshold segmentation 
and K-means segmentation. It is showed in the experiments that the novel approach is very effective for extracting the block objects 
in remote sensing images. 
1. INTRODUCTION 
With a large number of high-resolution remote sensing images 
being acquired, based on remote sensing images automatically 
updated GIS data, as well as the important status in military and 
civilian and so to, there is urgent request in automatic target 
recognition of remote sensing images. So far, the bottleneck in 
the digital photogrammtry, remote sensing technology and 
application and machine vision areas is still image extraction 
and automatic target recognition. Image extraction and 
recognition is the object extraction from the image data by 
manual or automatic / semi-automatic way extracting the 
identifying target, including artificial and natural features 
objectives. From the original images through a variety of 
mathematical algorithms extracting, segmenting and classifying 
image features, such as the structural features, edge features, 
texture features, shadow features is the basis of object 
recognition. Interpretation of remote sensing images is 
interpretation of images and classifying the target by artificial or 
computer methods. Interpretation of remote sensing images can 
use all kinds of statistic methods based on gray of the image, 
use the classification methods based on image characteristics, 
and reason from the image understanding and various 
knowledge. These methods can be combined to form mutual 
various intelligence methods. Multi-scale wavelet remote 
sensing image segmentation (1996; Li, 2000; Chio et al., 2001; 
Fosgate et al., 1997; Rezaee et al., 2000; Song, 2004) is based 
on Mallat fast transform of discrete wavelets, extracting in 
various scales the high-frequency characteristics of images in 
the horizontal , vertical and diagonal direction, with the low- 
frequency information of images wavelet transform in the same 
scale composing wavelet feature tree, in different scales window 
segmenting, gradually acquired segmentation results at fine 
scale. Image features, in particular texture features, may be in 
any direction. Image analysis methods based on Mallat 
transform of wavelet can not describe the other direction 
features. In the process of remote sensing image object 
extraction, sometimes we are interested in features of the 
certain direction (for example: along the road heading, building 
parallel direction, etc.), here the limitation of multi-scale 
wavelet remote sensing image analysis is on the surface. In the 
classification and segmentation of remote sensing image, there 
exists certain conjunction and influence between images 
features and this effect is likely to be nonlinear. Usually 
neighboring segmentation algorithms do not take into account 
this character of the image features. Bayesian classifier 
(Pemkopf, 2005; Romberg et al., 2001; Bouman et al., 1996) 
regards image function as realization of Random field X, based 
on the maximum similar the features with the same probability 
density function been marked or clustering. Bayesian classifier 
takes into account the interrelated relations between the images 
features, but it needs to know joint probability density between 
imaging features. Authors propose a novel approach in which 
remote images may be segmented in two steps based on the 
directional wavelet transform at multi-scale. 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 SOM (Doucette 
et al., 2001; Haykin, 2001; Kohonen et al., 1996; Kohonen, 
1997). SOM is a two-tier network architecture, including input 
models layer and competition output layer. Feature vectors of 
input image in the process of network disposing the winner will 
be priority marked, classified and until competition level output. 
The character of SOM side inhibition not only take into account 
the non-linear relationship between the images features, and 
also make sure the winners priority marked. In the paper 
experiment uses histogram threshold segmentation, K-means 
segmentation and multi-scale self-organizing image 
segmentation for comparing. It is showed in the experiments
	        
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