Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

Qi Wang, Jianzhe Lin, Yuan Yuan

Research output: Contribution to journalArticlepeer-review

468 Scopus citations

Abstract

Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.

Original languageEnglish
Article number7436783
Pages (from-to)1279-1289
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume27
Issue number6
DOIs
StatePublished - Jun 2016

Keywords

  • Band selection
  • deep learning
  • hyperspectral image (HSI) classification
  • manifold ranking (MR)
  • saliency
  • Stacked autoencoders (SAEs)

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