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

作者：Zabalza, Jaime; Ren, Jinchang^*; Yang, Mingqiang; Zhang, Yi; Wang, Jun; Marshall, Stephen; Han, Junwei

来源：ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 93: 112-122.

DOI：10.1016/j.isprsjprs.2014.04.006

• Folded Principal Component Analysis (F-PCA)
• Feature extraction
• Data reduction
• Hyperspectral Imaging (HSI)
• Support Vector Machine (SVM)
• Remote sensing

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.

• 单位
  西北工业大学; 天津大学; 山东大学; 北京航空航天大学