Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing

Jaime Zabalza, Jinchang Ren, Mingqiang Yang, Yi Zhang, Jun Wang, Stephen Marshall, Junwei Han

Research output: Contribution to journalArticle

102 Citations (Scopus)
365 Downloads (Pure)


As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used.
Original languageEnglish
Pages (from-to)112-122
Number of pages11
JournalISPRS Journal of Photogrammetry and Remote Sensing
Early online date20 May 2014
Publication statusPublished - Jul 2014



  • folded principal component analysis
  • feature extraction
  • data reduction
  • hyperspectral imaging
  • support vector machine
  • remote sensing

Cite this