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Author (up) Udelhoven, T.; van der Linden, S.; Waske, B.; Stellmes, M.; Hoffmann, L. openurl 
  Title Hypertemporal Classification of Large Areas Using Decision Fusion Type Journal Article
  Year 2009 Publication Geoscience and Remote Sensing Letters, IEEE Abbreviated Journal  
  Volume Pp Issue 99 Pages 592-596  
  Keywords Accuracy; Decision fusion (DF); Image resolution; National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR); Remote sensing; Satellites; Spatial resolution; Support vector machines; Vegetation mapping; hypertemporal data; large-area mapping; support vector machines (SVMs)  
  Abstract A novel multiannual land-cover-classification scheme for classifying hypertemporal image data is suggested, which is based on a supervised decision fusion (DF) approach. This DF approach comprises two steps: First, separate support vector machines (SVMs) are trained for normalized difference vegetation index (NDVI) time-series and mean annual temperature values of three consecutive years. In the second step, the information of the preliminary continuous SVM outputs, which represent posterior probabilities of the class assignments, is fused using a second-level SVM classifier. We tested the approach using the 10-day maximum-value NDVI composites from the “Mediterranean Extended Daily One-km Advanced Very High Resolution Radiometer Data Set” (MEDOKADS). The approach increases the classification accuracy and robustness compared with another DF method (simple majority voting) and with a single SVM expert that is trained for the same multiannual periods. The results clearly demonstrate that DF is a reliable technique for large-area mapping using hypertemporal data sets.  
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  ISSN 1545-598x ISBN Medium  
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  Call Number geomatics @ Udelhoven2009 Serial 187  
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