Characterizing uncertainty and enhancing utility in remotely sensed land cover using error matrices localized in canonical correspondence analysis ordination space

HIGHLIGHTS

• who: Yue Wan and collaborators from the College of Resource and Environment, Henan Agricultural University, Zhengzhou, China have published the paper: Characterizing Uncertainty and Enhancing Utility in Remotely Sensed Land Cover Using Error Matrices Localized in Canonical Correspondence Analysis Ordination Space, in the Journal: (JOURNAL) of 27/Dec/2022
• what: This study seeks to analyze map-reference class co-occurrences locally so that not only accuracy indicators but also map-reference cover type co-occurrences, transitions, and marginals (reference class probabilities) can be predicted, all at individual pixels. This research proposes constructing error matrices localized . . .
  

   

  

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