PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification

Yijun Yan, Jinchang Ren, Qiaoyuan Liu, Huimin Zhao, Haijiang Sun, Jaime Zabalza

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
27 Downloads (Pure)

Abstract

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this paper a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded-PCA are utilized to fuse with the 2DSSA, as folded-PCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational complexity has been significantly reduced whilst preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, the proposed approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully demonstrated the superiority of the proposed approach, in comparison to several state-of-the-art HSI classification methods and deep-learning models.
Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume20
Early online date19 Oct 2021
DOIs
Publication statusE-pub ahead of print - 19 Oct 2021

Keywords

  • hyperspectral image (HSI)
  • spectral-spatial feature mining
  • principal component analysis (PCA)
  • singular spectrum analysis (SSA)

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