Soft Computing

, Volume 21, Issue 23, pp 7053–7065 | Cite as

SVM or deep learning? A comparative study on remote sensing image classification

  • Peng Liu
  • Kim-Kwang Raymond Choo
  • Lizhe Wang
  • Fang Huang
Methodologies and Application

Abstract

With constant advancements in remote sensing technologies resulting in higher image resolution, there is a corresponding need to be able to mine useful data and information from remote sensing images. In this paper, we study auto-encoder (SAE) and support vector machine (SVM), and to examine their sensitivity, we include additional umber of training samples using the active learning frame. We then conduct a comparative evaluation. When classifying remote sensing images, SVM can also perform better than SAE in some circumstances, and active learning schemes can be used to achieve high classification accuracy in both methods.

Keywords

Spatial big data Sparse auto-encoder Support vector machine Active learning Remote sensing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Peng Liu
    • 1
  • Kim-Kwang Raymond Choo
    • 3
  • Lizhe Wang
    • 1
    • 2
  • Fang Huang
    • 4
  1. 1.Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.School of Computer Science China University of GeoscienceWuhanPeople’s Republic of China
  3. 3.School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia
  4. 4.School of Resources and EnvironmentUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

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