Abstract
Original spectral features contain information pertinent to certain target spectral features. Without an efficient spectral feature extraction method, the target detection performance might be degraded. We present spectral feature extraction techniques based on the Fourier domain for use in target detection. These feature extraction methods are the Fourier magnitude (FM), Fourier phase (FP), and Fourier coefficient selection (FCS) methods. In our target detection experiments, we compared the proposed methods to the principle component analysis (PCA) and independent component analysis (ICA) methods and the original spectral features. The experiment results show that the FCS target detection accuracy is 95.75%, whereas the accuracies of the FM, FP, PCA, ICA methods, and the original spectral features are 86.76%, 36.28%, 84.51%, 74.49%, and 78.92%, respectively. The average feature extraction times of the proposed methods are 223% faster than that found for the PCA and 304% faster than the ICA methods.
| Original language | English |
|---|---|
| Article number | 111704 |
| Journal | Optical Engineering |
| Volume | 51 |
| Issue number | 11 |
| DOIs | |
| State | Published - Nov 2012 |
Bibliographical note
Funding Information:This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2012-0001773), and in part by the Defense Acquisition Program Administration and Agency for Defense Development, Korea, through the Image Information Research Center at Korea Advanced Institute of Science and Technology under the contract UD100006CD, and in part by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012-0005858), and in part by Universiti Teknikal Malaysia Melaka.
Keywords
- Dimensionality reduction
- Fourier spectral feature
- Hyperspectral image
- Target detection