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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tjiong, A.S.J. Monteiro, S.T. |
| Copyright Year | 2011 |
| Description | Author affiliation: Australian Centre for Field Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, NSW 2006, Australia (Tjiong, A.S.J.; Monteiro, S.T.) |
| Abstract | Hyperspectral image data has great potential to identify and classify the chemical composition of materials remotely. Factors limiting the use of hyperspectral sensors in practical land-based applications, such as robotics and mining, are the complexity and cost of data acquisition, and the processing time required for the subsequent analysis. This is mainly due to the high dimensional and high volume nature of hyperspectral image data. In this paper, we propose to combine a feature selection method, based on particle swarm optimization (PSO), with a kernel method, support vector machines (SVM), to reduce the dimensionality of hyperspectral data for classification. We evaluate several different kernels, including some optimized for hyperspectral analysis. In particular, a recent kernel called observation angle dependent (OAD) kernel, originally designed for Gaussian Process regression, was extended for SVM classification. The SVM with the optimized kernel was then applied to induce the feature selection of a binary version of PSO. We validate the method using hyperspectral data sets acquired of rock samples from Western Australia. The empirical results demonstrate that our method is able to efficiently reduce the number of features while keeping, or even improving, the performance of the SVM classifier. |
| Starting Page | 1762 |
| Ending Page | 1769 |
| File Size | 1052633 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424478347 |
| e-ISBN | 9781424478354 |
| DOI | 10.1109/CEC.2011.5949828 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-05 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Support vector machines Hyperspectral imaging Measurement Scattering Accuracy |
| Content Type | Text |
| Resource Type | Article |
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