• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Combining Unmixing and Deep Feature Learning for Hyperspectral Image Classification

    Thumbnail
    View/Open
    AlamPUB3215.pdf (1.934Mb)
    Author(s)
    Alam, Fahim Irfan
    Zhou, Jun
    Tong, Lei
    Liew, Alan Wee-Chung
    Gao, Yongsheng
    Griffith University Author(s)
    Gao, Yongsheng
    Liew, Alan Wee-Chung
    Zhou, Jun
    Year published
    2017
    Metadata
    Show full item record
    Abstract
    Image classification is one of the critical tasks in hyperspectral remote sensing. In recent years, significant improvement have been achieved by various classification methods. However, mixed spectral responses from different ground materials still create confusions in complex scenes. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection of spectral signatures. Considering the usefulness of these techniques, we propose to utilize the unmixing results as an input to classifiers for better classification accuracy. We propose a novel band group based structure ...
    View more >
    Image classification is one of the critical tasks in hyperspectral remote sensing. In recent years, significant improvement have been achieved by various classification methods. However, mixed spectral responses from different ground materials still create confusions in complex scenes. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection of spectral signatures. Considering the usefulness of these techniques, we propose to utilize the unmixing results as an input to classifiers for better classification accuracy. We propose a novel band group based structure preserving nonnegative matrix factorization (NMF) method to estimate the individual spectral responses from different materials within different ranges of wavelengths. Then we train a convolutional neural network (CNN) with the unmixing results to generate powerful features and eventually classify the data. This method is evaluated on a new dataset and compared with several state-of-the-art models, which shows the promising potential of our method.
    View less >
    Conference Title
    2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
    Volume
    2017-December
    DOI
    https://doi.org/10.1109/DICTA.2017.8227419
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/377986
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander