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PICTURE CLASSIFICATION AND SEGMENTATION BY FRATURE COMBINATION 
Ta IN MULTISPECTRAL DATA 
TE. Mauer, R. Schürf 
Forschungsinstitut für Informationsverarbeitung 
und Mustererkennung 
Breslauer Straße 48, D-7500 Karlsruhe 1 
ABSTRACT 
The principal methods for image classification and image 
segmentation, which have been developped until now, work with 
a separate use of multispectral and textural properties. However 
those methods leads to satisfactory results only in some specific 
domains or in image which are mainly composed by uniform types 
of either spectral or textural objects. 
Invenstigations show that some methods have complementary 
properties and are able to support one another. Considering the 
performance of classification in more general applications, the 
combination of such complementary methods promises an essential 
improvement in picture processing. 
As a result of the comparative investigations between the 
different methods a proposal of a processing system is described. 
This segmentation and classification system processes real images 
without a priori knowledge combining the complementary methods. 
A possible application is the production of thematic maps to 
planning and ecologic aims, for example by evaluation of image 
data from satellites. : 
INTRODUCTION 
Most of the developped methods for image analysis, segmentation 
and automatic classification evaluate either multispectral 
features or textural properties, but not simultaneously. The 
separate use of one of these methods is successful at special 
problems of picture processing and for images which are composed 
of uniform types of objects. Most of the real images contain both 
types of objects together. Considering the performance of 
classification in more general applications, the results of the 
separate use of oniy one of these methods are not satisfactory. 
Therefore the combination of both methods promises an essential 
improvement in picture processing. 
This paper describes the advantages and shortcommings of the 
multispectral analysis and the texture analysis. It will be 
demonstrated that the multispectral image processing is able to 
support the texture analysis, and a combination of these methods 
is proposed. In order to simplify such a combination, the train- 
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