PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral Classification

Yijun Yan, Jinchang Ren (Lead / Corresponding author), Qiaoyuan Liu, Huimin Zhao (Lead / Corresponding author), Haijiang Sun, Jaime Zabalza

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
36 Downloads (Pure)

Abstract

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral- and spatial-domain feature extraction in hyperspectral images (HSIs). However, PCA itself suffers from low efficacy if no spatial information is combined, while 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this letter a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded PCA (FPCA) are fused with the 2DSSA, as FPCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational cost can be significantly reduced while preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, our 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 validated the superiority of the proposed approach, in comparison to several state-of-the-art methods and deep learning models.

Original languageEnglish
Article number5505405
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume20
DOIs
Publication statusPublished - 19 Oct 2023

Keywords

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

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

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