An improved semi-supervised local discriminant analysis for feature extraction of hyperspectal image

Renbo Luo, Wenzhi Liao, Wilfried Philips, Youguo Pi

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

We propose an improved semi-supervised local discriminant analysis (ISELD) for feature extraction of hyperspectral image in this paper. The proposed ISELD method aims to find a projection which can preserve local neighborhood information and maximize the class discrimination of the data. Compared to the previous SELD, the proposed ISELD better models the correlation of labeled and unlabeled samples. Experimental results on an ROSIS urban hyperspectral image are encouraging. Compared to some recent feature extraction methods, our approach has more than 2% improvements as the training sample size changes.
Original languageEnglish
Number of pages4
DOIs
Publication statusPublished - 11 Jun 2015
Event2015 JOINT URBAN REMOTE SENSING EVENT (JURSE) - Lausanne, Switzerland
Duration: 30 Mar 20151 Apr 2015

Conference

Conference2015 JOINT URBAN REMOTE SENSING EVENT (JURSE)
Abbreviated titleJURSE 2015
CountrySwitzerland
CityLausanne
Period30/03/151/04/15

Keywords

  • feature extraction
  • soil
  • asphalt
  • principal component analysis
  • hyperspectral imaging
  • image recognition
  • learning (artificial intelligence)
  • remote sensing

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