Detecting pine wilt disease at the pixel level from high spatial and spectral resolution UAV-borne imagery in complex forest landscapes using deep one-class classification

This publication has 29 references indexed in Scilit:

• Hyperspectral Image Classification With Deep Learning Models
  IEEE Transactions on Geoscience and Remote Sensing, 2018
• Estimate global risks of a forest disease under current and future climates using species distribution model and simple thermal model – Pine Wilt disease as a model case
  Forest Ecology and Management, 2018
• Potential distribution of pine wilt disease under future climate change scenarios
  PLOS ONE, 2017
• Assessments of iodoindoles and abamectin as inducers of methuosis in pinewood nematode, Bursaphelenchus xylophilus
  Scientific Reports, 2017
• Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
  Remote Sensing, 2017
• Towards Open Set Deep Networks
  2016
• Hierarchical feature learning with dropout k-means for hyperspectral image classification
  Neurocomputing, 2016
• One-Class versus Binary Classification: Which and When?
  2012
• Pine Wilt Disease: A Worldwide Threat to Forest Ecosystems
  2008
• Learning from positive and unlabeled examples
  Theoretical Computer Science, 2005