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AN EFFICIENT MULTI-SCALE SEGMENTATION FOR HIGH-RESOLUTION REMOTE 
SENSING IMAGERY BASED ON STATISTICAL REGION MERGING AND MINIMUM 
HETEROGENEITY RULE 
H. T. Li\ H.Y. Gu a,b , Y. S. Han 3 , J. H. Yang 3 , S. S. Han b 
institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, Beijing, 100039, 
P.R.China. 
b Institnte of Surveying and Geography Science, Liaoning Technical University, 47 Zhonghua Road, Liao Ning 123000, 
P.R.China. 
Commission IV, WG IV/9 
KEY WORDS: Multi-scale Segmentation, High-Resolution, Statistical Region Merging (SRM), Minimum Heterogeneity Rule 
(MHR), Fractal Net Evolution Approach (FNEA) 
ABSTRACT: 
Multi-scale segmentation is an essential step toward higher level image processing in remote sensing. This paper presents a new 
multi-scale segmentation method based on Statistical Region Merging (SRM) for initial segmentation and Minimum Heterogeneity 
Rule (MHR) for merging objects where high resolution (HR) QuickBird imageries are used. It synthesized the advantages of SRM 
and MHR. The SRM segmentation method not only considers spectral, shape, scale information, but also has the ability to cope with 
significant noise corruption, handle occlusions. The MHR used for merging objects takes advantages of its spectral, shape, scale 
information, and the local, global information. Compared with Fractal Net Evolution Approach (FNEA) eCognition adopted and 
SRM methods, the results showed that the proposed method overcame the disadvantages of them and was an effective multi-scale 
segmentation method for HR imagery. 
1. INTRODUCTION 
Image segmentation is the process of dividing an image into 
homogenous regions, which is an essential step toward higher 
level image processing such as image analysis, pattern 
recognition and automatic image interpretation (Blaschke and 
Strobl, 2001). So far, there are over 1000 kinds of segmentation 
approaches developed (Zhang, 2001). General segmentation 
methods include global behaviour-based and local behaviour- 
based methods (Kartikeyan, et al., 1998). Global behaviour- 
based methods group the pixels based on the analysis of the 
data in the feature space. Typical examples are clustering and 
histogram threshold. Local behaviour-based methods analyze 
the variation of spectral features in a small neighbourhood. 
Typical examples are edge detection and region extraction (Fu 
and Mui, 1981). 
However, not all of the segmentation techniques are feasible for 
High-Resolution (HR) imagery due to the following facts: 
(1) The HR imagery is multi-spectral and multi-scale, so both 
the complexity and redundancy are increased obviously; 
(2) The HR imagery provides the more details such as 
spectral, shape, context and texture. The traditional 
segmentation algorithm is only based on the colour 
information and can not provide the satisfying results; 
(3) Different class has its inherent feature in different scale. 
For example, at coarse scales we may find fields, while at 
finer scales we may find individual trees or plants. So the 
segmentation model on one scale needs to be modified 
when used on the other scale. 
is important to segment imagery effectively with all kinds of 
information and object character. Recently, several authors 
have proposed multi-scale segmentation algorithms for HR 
imagery (Chen, 2003; Cheng, 2005; Sramek, 1997). A majority 
of image segmentation algorithms are based on region growing 
methods which take some pixels as seeds and grow the regions 
around them based on certain homogeneity criteria. The 
commercial software, eCognition, adopts Fractal Net Evolution 
Approach (FNEA) for segmentation. FNEA is a region growing 
technique based on local criteria and starts with one pixel image 
objects. Image objects are pairwise merged one by one to form 
bigger objects. The merging criterion is that average 
heterogeneity of image objects weighted by their size in pixels 
should be minimized (Baatz and Schape, 2000; Benz, et al., 
2004). 
However, the local region growing technique has some 
limitations: 
(1) It is not efficient in both computation and memory; 
(2) It has some difficulties in gathering a set of seeds and an 
adequate homogeneity criterion; 
(3) It depends on the choice of starting point and the order in 
which the pixels and regions are examined; 
(4) It is hard to find coincident boundary because one pixel 
image object merges with another without respect of 
adjacent pixels. 
In order to overcome the disadvantages of segmentation from 
one pixel and get more accurate segmentation result, several 
authors suggest merging bigger objects generated by initial 
segmentation, which avoids of the disadvantages of region 
growing method from single pixel. 
Therefore, owing to the HR imagery is multi-spectral and multi 
scale, and includes more details and information of the object, it