Supervised Wishart Classifier for Rice Mapping Using Multi-temporal Envisat Asar Aps Data

نویسندگان

  • E. X. Chen
  • Z. Y. Li
  • B. X. Tan
  • Y. Pang
  • X. Tian
  • B. B. Li
  • Jong-Sen Lee
چکیده

For the operational application of multi-temporal ENVISAT ASAR APS data to rice mapping, a complex Wishart distribution based multi-temporal classifier was evaluated in this paper. The classification accuracy of this classifier was quantitatively compared with commonly used classifiers for optical remote sensing image classification including maximum likelihood classifier and minimum euclidean classifier with only intensity SAR images being used for classification. It has been shown that classification accuracy can be improved significantly if the Wishart classifier is applied to the multi-temporal dual polarization datasets instead of applying it to one dual polarization dataset; Wishart classifier achieved the highest overall classification accuracy, which is recommended for operational rice mapping using multi-temporal ENVISAT APS data.

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تاریخ انتشار 2007