Unsupervised band selection for hyperspectral imagery classification without manual band removal

Sen Jia, Zhen Ji, Yuntao Qian, Linlin Shen

Research output: Journal PublicationArticlepeer-review

138 Citations (Scopus)

Abstract

The rich information available in hyperspectral imagery has provided significant opportunities for material classification and identification. Due to the problem of the "curse of dimensionality" (called Hughes phenomenon) posed by the high number of spectral channels along with small amounts of labeled training samples, dimensionality reduction is a necessary preprocessing step for hyperspectral data. Generally, in order to improve the classification accuracy, noise bands generated by various sources (primarily the sensor and the atmosphere) are often manually removed in advance. However, the removal of these bands may discard some important discriminative information, eventually degrading the classification accuracy. In this paper, we propose a new strategy to automatically select bands without manual band removal. Firstly, wavelet shrinkage is applied to denoise the spatial images of the whole data cube. Then affinity propagation, which is a recently proposed feature selection approach, is used to choose representative bands from the noise-reduced data. Experimental results on three real hyperspectral data collected by two different sensors demonstrate that the bands selected by our approach on the whole data (containing noise bands) could achieve higher overall classification accuracies than those by other state-of-the-art feature selection techniques on the manual-band-removal (MBR) data, even better than the bands identified by the proposed approach on the MBR data, indicating that the removed "noise" bands are valuable for hyperspectral classification, which should not be eliminated.

Original languageEnglish
Article number6165389
Pages (from-to)531-543
Number of pages13
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume5
Issue number2
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Affinity propagation
  • band selection
  • hyperspectral imagery classification
  • wavelet shrinkage

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

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