An unsupervised saliency-guided deep convolutional neural network for accurate burn mapping from sentinel-1 sar data

HIGHLIGHTS

• who: Ali Radman and collaborators from the School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran have published the research work: An Unsupervised Saliency-Guided Deep Convolutional Neural Network for Accurate Burn Mapping from Sentinel-1 SAR Data, in the Journal: (JOURNAL)
• what: The authors assess the potential of Sentinel-1 SAR images for precise forest-burned area mapping using networks (DCNN). This study proposes a fully automated, unsupervised burned area mapping approach that can be conducted at a reasonable computational cost. An(F1), Adam Four evaluation factors, including precision . . .
  

   

  

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