Multiscale superpixel-level subspace-based support vector machines for hyperspectral image classification

Haoyang Yu, Lianru Gao, Wenzhi Liao, Bing Zhang, Aleksandra Pizurica, Wilfried Philips

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

This letter introduces a new spectral-spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such that the pixel-level data can be represented by multiscale superpixel-level (MSP) data sets. Then, a subspace-based support vector machine (SVMsub) is adopted to obtain the classification maps with multiscale inputs. Finally, the classification result is achieved via a decision fusion process. The resulting method, called MSP-SVMsub, makes use of the spatial and spectral coherences, and contributes to better feature characterization. Experimental results based on two real hyperspectral data sets indicate that the MSP-SVMsub exhibits good performance compared with other related methods.
Original languageEnglish
Pages (from-to)2142-2146
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume14
Issue number11
DOIs
Publication statusPublished - 4 Oct 2017

Keywords

  • hyperspectral image classification
  • multiscale superpixel segmentation
  • subspace projection
  • support vector machines (SVM)
  • principal component analysis
  • geophysical image processing

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