Spectral-spatial classification of hyperspectral image based on locality preserving discriminant analysis

Min Han, Chengkun Zhang, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, a spectral-spatial classification method for hyperspectral image based on spatial filtering and feature extraction is proposed. To extract the spatial information that contain spatially homogeneous property and distinct boundary, the original hyperspectral image is processed by an improved bilateral filter firstly. And then the proposed feature extraction algorithm called locality preserving discriminant analysis, which can explore the manifold structure and intrinsic characteristics of the hyperspectral dataset, is used to reduce the dimensionality of both the spectral and spatial features. Finally, a support vector machine with a composite kernel is used to examine the performance of the proposed methods. Experiments results on a hyperspectral dataset demonstrate the effectiveness of the proposed algorithm in the classification tasks.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings
EditorsLong Cheng, Qingshan Liu, Andrey Ronzhin
PublisherSpringer Verlag
Pages21-29
Number of pages9
ISBN (Print)9783319406626
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event13th International Symposium on Neural Networks, ISNN 2016 - St. Petersburg, Russian Federation
Duration: 6 Jul 20168 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9719
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Symposium on Neural Networks, ISNN 2016
Country/TerritoryRussian Federation
CitySt. Petersburg
Period6/07/168/07/16

Keywords

  • Feature extraction
  • Hyperspectral
  • Manifold structure
  • Spatial filtering
  • Support vector machine with a composite kernel

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