IDEAS home Printed from https://ideas.repec.org/a/igg/jitwe0/v16y2021i1p56-74.html
   My bibliography  Save this article

Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

Author

Listed:
  • Weiwei Yang

    (School of Electronics and Information Engineering (School of Big Data Science), Taizhou University, China)

  • Haifeng Song

    (School of Electronics and Information Engineering (School of Big Data Science), Taizhou University, China)

Abstract

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

Suggested Citation

  • Weiwei Yang & Haifeng Song, 2021. "Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 16(1), pages 56-74, January.
    • Handle: RePEc:igg:jitwe0:v:16:y:2021:i:1:p:56-74
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITWE.2021010103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jitwe0:v:16:y:2021:i:1:p:56-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.