1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification

      Preprint
      ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In the proposed SEHybridSN model, a dense block was used to reuse shallow feature and aimed at better exploiting hierarchical spatial spectral feature. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial spectral features was realized by the channel attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer. Experiment results indicate that our proposed model learn more discriminative spatial spectral features using very few training data. SEHybridSN using only 0.05 and 0.01 labeled data for training, a very satisfactory performance is obtained.

          Related collections

          Author and article information

          Journal
          19 November 2021
          Article
          2111.10293
          82805990-2b18-4c02-a028-9b9e7fa8872d

          http://creativecommons.org/licenses/by-nc-nd/4.0/

          History
          Custom metadata
          arXiv admin note: text overlap with arXiv:1902.06701 by other authors
          cs.CV cs.LG eess.IV

          Computer vision & Pattern recognition,Artificial intelligence,Electrical engineering

          Comments

          Comment on this article