Zabalza, Jaime and Ren, Jinchang and Yang, Mingqiang and Zhang, Yi and Wang, Jun and Marshall, Stephen and Han, Junwei (2014) Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 93. pp. 112-122.
Folded_PCA_v12.pdf - Preprint
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.
|Keywords:||folded principal component analysis, feature extraction, data reduction, hyperspectral imaging , support vector machine, remote sensing, Electrical engineering. Electronics Nuclear engineering, Engineering (miscellaneous), Atomic and Molecular Physics, and Optics, Computers in Earth Sciences, Computer Science Applications, Geography, Planning and Development|
|Subjects:||Technology > Electrical engineering. Electronics Nuclear engineering|
|Department:||Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset Management
|Depositing user:||Pure Administrator|
|Date Deposited:||30 May 2014 09:02|
|Last modified:||21 Apr 2017 19:12|