Land Cover Classification based on the Universal Pattern Decomposition Method
نویسندگان
چکیده
* Corresponding author. E-mail: [email protected] Abstract – The universal pattern decomposition method (UPDM) has been successfully applied to simulated data for Landsat/ETM+, Terra/MODIS, ADEOS-II/GLI and others using ground-measured data. The UPDM is tailored to decrease dimensions of hyper multi-spectral data that have sensor-independent characteristics and thus exploit hyper multi-spectral remotely sensed data classification. In this study, we classified Landsat/ETM+ data via a transformation of the original reflectance spectral space to the UPDM subspace. Classification accuracy was compared using results from UPDM and the primary component transformation (PCT). Classification results for ETM+ data were also compared using several traditional classifiers. The UPDM and the PCT showed similar classification accuracy. The UPDM sub-space has definitive physical meanings. Classification results using UPDM are sensor-independent, which are very significant for comparison of results derived from different data.
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