Feature reduction in a remote sensing dataset is often desirable to decrease the processing
time required to perform a classification and improve overall classification accuracy. This work introduces
a feature reduction method based on the singular value decomposition (SVD). This feature reduction
technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery
acquired over a forested area in Virginia, USA and Rondonia, Brazil. Subsequent parallel iterative guided
spectral class rejection (pIGSCR) forest/nonforest
classifications were performed to determine the quality
of the feature reduction. The classifications of the Virginia data were five times faster using SVDbased
feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also
compared to feature reduction using principal components analysis (PCA). The highest average accuracies
for the Virginia dataset (88.34%) and for the Rondonia dataset (93.31%) were achieved using the SVD.
The results presented here indicate that SVDbased
feature reduction can produce statistically significantly
better classifications than PCA