Urban Image Classification With Semisupervised Multiscale Cluster Kernels

Devis Tuia, Gustavo Camps-Valls

Research output: Contribution to journalArticleAcademicpeer-review

60 Citations (Scopus)

Abstract

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between la-beled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Original languageEnglish
Pages (from-to)65-74
Number of pages10
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume4
Issue number1
DOIs
Publication statusPublished - Mar 2011
Externally publishedYes

Keywords

  • Clustering
  • image classification
  • kernel methods
  • support vector machine (SVM)
  • urban monitoring
  • very high resolution (VHR)

Fingerprint Dive into the research topics of 'Urban Image Classification With Semisupervised Multiscale Cluster Kernels'. Together they form a unique fingerprint.

Cite this