Jalal Amini 
  
Imagemap simplification using mathematical morphology 
M.R.Saradjian |J. Amini, * 
Department of Surveying Engineering, Faculty of Engineering y 
Tehran University, Tehran, IRAN. 
, Department of Research, National Cartographic Center (N.C.C) 
Tehran, IRAN. 
Jalal in yahoo.com 
KEY WORDS: Morphology, Photogrammetry, Remote sensing, Segmentation, Simplification, ^ Thinning. 
ABSTRACT 
For a variety of mapping applications, images are the most important primary data sources. In photogrammetry 
and remote sensing, particular procedures are used for image and map data integration in order to perform change 
detection and automatic object extraction. The recognition of an object in an image is a complex task that involves 
a broad range of techniques. In general, three steps are used in this study. The first step is segmentation to object 
regions of interest. In this step, regions which may contain unknown objects, have to be detected. The second 
step focuses on the extraction of suitable features and then extraction of objects. The main purpose of feature 
extraction is to reduce data by means of measuring certain features that distinguish the input patterns. The final 
step is classification. It assigns a label to an object based on the information provided by its descriptors. 
At the end of segmentation stage, the images are too still complex. So it is necessary to simplify image for further 
processes. 
In this paper, investigation is made on the mathematical morphology operators for simplification of a gray-scale 
image or imagemap. Then an structure element, ( L^ ) , is applied on binary images to extract the skeletonized 
image. In this stage, there will remain lots of skeletal legs in the resultant image. Then in the next step, another 
structure element, ( E ^), is applied on skeletonized image to remove the remaining skeletal legs. The resulting 
thinned image may be extracted and then converted to vectors. The vector data may be input to a geographic 
information system (GIS) for further analysis. The program for this project is developed in visual c++ language 
under windows 98 operating system. 
1 INTRODUCTION 
One of the most fascinating promises of digital photogrammetry is the highly automated acquisition and updating 
of spatial data from images. Remarkable progress has been made in areas involving image and template matching 
such as automatic interior orientation, relative orientation, tie point selection, digital terrain model (DTM) 
generation and orthoimage generation. Although the current level of automation on most digital photogrammetric 
stations is still fairly low, a number of these developments are meanwhile available on some commercial systems 
(Gruen, 1996;Miller etal,1996; Walker and Petri, 1996). 
On the way towards automatic mapping or spatial data acquisition and update, automatic identification and 
localization of cartographic objects in aerial and satellite images has gained increasing attention in recent years. 
Despite the reports of. some achievements, the automatic extraction of man-made objects in essence is still an 
unresolved issue. The recognition of an object (i.e. building, roads,etc.) is a complex task that involves a broad 
range of techniques. In this paper the analysis is organized into three steps: segmentation, features extraction and 
classification. 
The first step which is the segmentation involves the identification of regions in an image that are homogeneous 
and dissimilar to all spatially adjacent regions. 
The second step is feature extraction. The purpose of feature extraction is to reduce data by measuring certain 
"features" or "properties" that distinguish input patterns. In feature extraction we transform an input observation 
vector to a feature vector using some orthogonal or nonorthogonal basis functions so that data in the feature 
space become uncorrelated. A variety of approaches have been developed for feature extraction. Commonly used 
feature space techniques include the Fourier Transform(FF), Moment feature space and etc,. The last step is 
classification. It assigns a label to an object based on the information provided by its descriptors provided from 
  
36 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000. 
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