Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing |
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| Institution: | 1. Centre for Excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom;2. School of Information Science and Technology, Shandong University, Jinan, China;3. School of Computer Software, Tianjin University, Tianjin, China;4. School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, China;5. School of Automation, Northwestern Polytechnical University, Xi''an, China;1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. German Aerospace Center (DLR), German Remote Sensing Data Center (DFD) Oberpfaffenhofen, 82234 Wessling, Germany;3. Department of Nature Protection and Research, Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany;4. Department of Environmental Science, Macquarie University, NSW 2109, Australia;1. College of Computer Science and Software Engineering, Shenzhen University, China;2. School of Computer Science and Technology, Nanjing University of Science and Technology, China;1. Image Processing Laboratory (IPL), Parc Científic, Universitat de València, 46980 Paterna, Spain;2. Secretary of Research and Postgraduate, CONACYT-UAN, 63155 Tepic, Nayarit, Mexico;1. School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China;2. Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China;3. Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Centre, Guangzhou 510006, China;4. Food Refrigeration and Computerized Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland |
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| Abstract: | 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. |
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| Keywords: | Folded Principal Component Analysis (F-PCA) Feature extraction Data reduction Hyperspectral Imaging (HSI) Support Vector Machine (SVM) Remote sensing |
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