A MULTIRESOLUTION REMOTELY SENSED IMAGE SEGMENTATION METHOD 
COMBINING RAINFALLING WATERSHED ALGORITHM AND FAST REGION 
MERGING 
Min Wang 3 
d Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 
Jiangsu, 210046, China - sysj0918@126.com 
KEY WORDS: Multiresolution, Image, Segmentation, Method, Algorithms 
ABSTRACT: 
Nowadays object oriented image analysis becomes a hot issue in the field of image processing and interpretation because of its more 
robust noise removing ability, more abundant image features and expertise knowledge involved in analysis. The first and most 
important step of object oriented image analysis is image segmentation, which segments an image into many visual homogenous 
parcels. Based on these parcels, which are ‘objects’ not ‘pixels’, more features can be involved which facilitates the succeeding 
image interpretation. In this work, a multi-resolution image segmentation method combining spectral and shape features is designed 
and implemented with reference to the basic ideas of eCognition, a famous object oriented image analyzing software package. The 
algorithm includes the following steps. 1) The initial segmentation parcels, so called the ‘sub feature units’ are obtained with 
rainfalling watershed algorithm for its fast speed and pretty good initial segmentation effects. 2) A fast region merging technique is 
designed to merge these sub feature units in a hierarchy way. A scale parameter is used to control the merging process, which stops a 
merge when the minimal parcel merging cost exceeds its power. A multi-resolution segmentation can be implemented with different 
scale parameters, for smaller scales means less cost while merging which create smaller parcels, and vice versa. Several experiments 
on high spatial resolution remotely sensed imagery are carried out to validate our method. 
1. INTRODUCTION 
Nowadays object oriented image analysis becomes a hot issue 
in the field of image process and interpretation. The basic idea 
of this kind of method is to segment an image into parcels, 
extract features from the parcels, and then complete the whole 
image interpretation with classifying the features. The main 
advantage of object oriented image analysis lies in that it deals 
with parcels, which are ‘objects’, not pixels, which causes more 
abundant features and spatial knowledge involved in analysis. 
Besides, with more robust pepper noise removing ability, it also 
brings more comprehensible interpretation results (Aplin et al., 
1999). eCognition (Definiens, 2007) is a world famous object 
oriented image analysis software, in which the multiresolution 
image segmentation method (Baatz et al., 2007) is a key and 
patented technology, whose technological details hasn’t been 
opened to the public yet. In order to implement our object- 
oriented image analysis software package for information 
extraction from high spatial resolution remotely sensed imagery, 
we design and implement a multiresolution image segmentation 
method combining spectral and shape features, with reference 
to the basic ideas of eCognition. Our method is validated with 
several successful experiments on high spatial resolution 
remotely sensed imagery. 2 
2. METHOD PRINCIPLE AND STEPS 
When grouping pixels into very small sub feature units at the 
beginning stage of our algorithm, it’s of little use to import 
shape feature. In our method, an initial segmentation is firstly 
carried out only with spectral features to obtain the sub feature 
units. Shape can then be introduced into the algorithm to 
control the further merging of these feature units with suitable 
size. We use rainfalling watershed algorithm to create these sub 
feature units for its fair segmentation precision and very fast 
algorithm speed, which is important for processing remotely 
sensed imagery commonly with large data volumes. But mainly 
due to image noise, most watershed algorithms including 
rainfalling watershed have a serious over-segmentation 
shortcoming. Sometimes it causes that there exist a large 
number of very small parcels scattered in the output 
segmentation. A pre or post image processing should be carried 
out to remove this adverse influence for further analysis. In our 
work, we take the latter one, which deals with these very small 
units in a unified region merging way. 
2.1 SUB FEATURE UNIT EXTRACTION 
Watershed algorithm is a pretty good image segmentation 
method based on image grey values. A classical implementation 
of watershed is based on immersion simulation [Vincent et al., 
1991]. Watershed segmentation can also be implemented in a so 
called rainfalling manner. Its principle is to find a steepest 
routine of every pixel on the simulated image topographic 
surface, and a watershed base is defined as the pixel set whose 
downriver routine ends at a same altitude local minimum. The 
algorithm includes two main steps: 1) flooding stage: flood the 
image with some altitude threshold to create partial ‘billabongs’ 
to reduce the high frequency signal parts caused by noises so to 
suppress the over-segmentation of common watershed 
algorithms; 2) rainfalling stage: in order to classify a pixel 
which hasn’t fallen into certain billabong, a rolling down route 
of a raindrop on that pixel is simulated, and all the pixels under 
this route will be grouped into one class (belong to a same 
watershed). After all these pixels are labelled, the segmentation 
will be terminated. A critical issue of rainfalling watershed 
segmentation implementation lies in correctly dealing with the 
local levels embedded in the slopes [Stoev, 2000].