Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning
作者: Xiaorui MaHongyu WangJie Wang
作者单位: 1School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, PR China
刊名: ISPRS Journal of Photogrammetry and Remote Sensing, 2016, Vol.120 , pp.99-107
来源数据库: Elsevier Journal
DOI: 10.1016/j.isprsjprs.2016.09.001
关键词: Hyperspectral imageSemisupervised classificationDeep learning
原始语种摘要: Abstract(#br)Semisupervised learning is widely used in hyperspectral image classification to deal with the limited training samples, however, some more information of hyperspectral image should be further explored. In this paper, a novel semisupervised classification based on multi-decision labeling and deep feature learning is presented to exploit and utilize as much information as possible to realize the classification task. First, the proposed method takes two decisions to pre-label each unlabeled sample: local decision based on weighted neighborhood information is made by the surrounding samples, and global decision based on deep learning is performed by the most similar training samples. Then, some unlabeled ones with high confidence are selected to extent the training set. Finally,...
全文获取路径: Elsevier  (合作)

  • learning 学识
  • image 
  • decision 决定
  • classification 分类
  • labeling 加标
  • feature 结构元件
  • based 基于
  • multi 多种