Feature extraction of hyperspectral images with semi-supervised graph learning

Renbo Luo, Wenzhi Liao, Xin Huang, Youguo Pi, Wilfried Philips, Jocelyn Chanussot

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

We propose a semisupervised graph learning (SEGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SEGL method aims to build a semisupervised graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised graph, we connect labeled samples according to their label information and unlabeled samples by their nearest neighborhood information. By sorting the mean distance between a unlabeled sample and labeled samples of each class, we connect the unlabeled sample with all labeled samples belonging to its nearest neighborhood class. Moreover, the proposed SEGL better models the actual differences and similarities between samples, by setting different weights to the edges of connected samples. Experimental results on four real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods.
Original languageEnglish
Pages (from-to)4389-4399
Number of pages11
JournalIEEE Journal of Selected Topics in Earth Observation and Remote Sensing
Volume9
Issue number9
DOIs
Publication statusPublished - 18 Feb 2016

Keywords

  • dimensionality reduction
  • nearest neighbor
  • remote sensing
  • linear discriminant analysis
  • semisupervised
  • classification
  • feature extraction
  • graph
  • hyperspectral images (HSIs)

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