Graph-based semi-supervised learning with weighted features for hyperspectral remote sensing image classification

HIGHLIGHTS

• who: Sensing Image Classification and collaborators from the ） University of Pavia （ PU): The second HSI dataset, named University of Pavia, was acquired in , at the University of Pavia, Italy, by the airborne Reflection Optical System Imaging Spectrometer sensor (ROSIS-03)The spatial size is, ×, pixels, the spatial resolution is, ., m, and , continuous bands are selected from the, .43~0., µm band, including , types of ground objects. Since the distribution of samples in the PU dataset is relatively balanced, this paper randomly selects , samples of each kind of ground object as training samples. Finally, samples are selected from . . .
  

   

  

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