EVALUATION OF CONVENTIONAL AND CONTEXTUAL CLASSIFICA TION 
TECHNIQUES TO ASSESS LAND COVER AND LAND USE IN MULTISPECTRAL IMAGES 
Laerte Guimaräes Ferreira Junior 
Eduardo Delgado Assad 
Heleno Bezerra 
Lucimar Moreira 
CPAC/EMBRAPA 
Laboratório de Física Ambiental 
BR 020 - Km 18 - Rod. BSB/Fortaleza 
73301-970 - Planaltina, DF, Brasil 
ISPRS Commission VII / Working Group 3 
ABSTRACT 
The possibility for producing land use and land cover maps through satellite images has been increased by the 
use of automatic image classification techniques. On the other hand the introduction of contextual classifiers 
as well as classifiers based on image segmentation greatly improved the accuracy of the classified images. 
These classifiers, on the contrary of per pixel classifiers such as the maximum likelihood and the minimum 
Euclidian distance , take into account not only the spectral information related to specific pixels, but also the 
location of these pixels as well as their surrounding spectral characteristics. 
In order to evaluate the performance of these new classifiers implemented in the SPRING geoprocessing 
system, contextual and segmentation based classifiers as well as the conventional classifiers were applied on 
both enhanced (linear contrast stretching and decorrelation) and raw LANDSAT-TM data. The selected area 
of approximately 62.5 km2, located in an environmental protection area near Brasilia - DF, comprises a 
typical urban-rural fringe, where the natural savanna woodland is being transformed into agricultural, cattle 
raising and re-forestation areas. The results already gathered have shown that the use of enhancement 
techniques increases the spectral separability of all scene's targets, leading to very similar results no matter 
which classifier is being used. On the other hand, when classification is not preceded by spectral 
enhancement, best results concerning pattern recognition are shown by the contextual maximum likelihood 
classifier. 
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