Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 9,
  • Issue 1,
  • pp. 011002-
  • (2011)

Combining clustering and classification for remote-sensing images using unlabeled data

Not Accessible

Your library or personal account may give you access

Abstract

A joint clustering and classification approach is proposed. This approach exploits unlabeled data for efficient clustering, which is applied in the classification with support vector machine (SVM) in the case of small-size training samples. The proposed method requires no prior information on data labels, and yields better cluster structures. Through cluster assumption and the notions of support vectors, the most confident k cluster centers and data points near the cluster boundaries are labeled and used to train a reliable SVM classifier. Our method gains better estimation of data distributions and mitigates the unrepresentative problem of small-size training samples. The data set collected from Landsat Thematic Mapper (Landsat TM-5) validates the effectiveness of the proposed approach.

© 2011 Chinese Optics Letters

PDF Article
More Like This
Semisupervised classification of hyperspectral images with low-rank representation kernel

Seyyed Ali Ahmadi and Nasser Mehrshad
J. Opt. Soc. Am. A 37(4) 606-613 (2020)

Feature weighting algorithms for classification of hyperspectral images using a support vector machine

Bin Qi, Chunhui Zhao, and Guisheng Yin
Appl. Opt. 53(13) 2839-2846 (2014)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved