SPECTRAL AND TEXTURAL ANALYSIS FOR IMPROVING 
CROP CLASSIFICATION ACCURACY 
Francisco Redondo 
Maria Cristina Serafini 
Miriam Antes 
Adrian Benitez 
Universidad Nacional de Lujan 
Dept. Ciencias Basicas 
Ruta 5 y Ruta 7 
Lujan - Provincia de Buenos Aires 6700 
Argentina 
ISPRS Commission VII / Working Group 3 
ABSTRACT 
The addition of low pass filtered images and textural features significantly improved class separability 
when compared to a spectral only classification. Low pass filtering removes high frequency image 
infornation and decreases the spread within each class signature. In consequence class overlapping 
decreases. On the other hand, textural information could be used only if the representative spatial 
variations of each cover are equal or higher than the spatial resolution of the sensor. Using TM and SPOT 
data and the procedures above described, an experience in a representative pilot area of the main 
agricultural region in Argentina was carried out. Results show an increase of more than 10 percent in the 
overall classification accuracy within the training sites. Another interesting find is that the accuracy was 
not decrease outside the training areas when low pass filtered images were used in the analysis. That 
improvement in the classifier consistency is very important when large areas are being studied. 
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