Land-use Classification Using Multitemporal Ers-1, Radarsat and Jers Sar-images

Land-use classification was performed by using a set of ERS-1, JERSand Radarsat images. Classes were water, forests (with subclasses according to stem volume), agricultural field, mire and urban area. Median filtering was used for speckle reduction and principal component analysis for feature extraction. Spectral classification was performed by using self-organizing feature map and learning vector quantization. Contextual classification was performed as postprocessing step. The overall accuracy of the spectral classification was 86.4% and the best contextual classification 89.8%.