Abstract
We investigate the use of sparse principal components for representing hyperspectral imagery when performing feature selection. For conventional multispectral data with low dimensionality, dimension reduction can be achieved by using traditional feature selection techniques for producing a subset of features that provide the highest class separability, or by feature extraction techniques via linear transformation. When dealing with hyperspectral data, feature selection is a time consuming task, often requiring exhaustive search of all the feature subset combinations. Instead, feature extraction technique such as PCA is commonly used. Unfortunately, PCA usually involves non-zero linear combinations or 'loadings' of all of the data. Sparse principal components are the sets of sparse vectors spanning a low-dimensional space that explain most of the variance present in the data. Our experiments show that sparse principal components having low-dimensionality still characterize the variance in the data. Sparse data representations are generally desirable for hyperspectral images because sparse representations help in human understanding and in classification.
Original language | English |
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Title of host publication | 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings |
Volume | 2 |
Edition | 1 |
DOIs | |
State | Published - 1 Dec 2008 |
Event | 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings - Boston, MA, United States Duration: 6 Jul 2008 → 11 Jul 2008 |
Other
Other | 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings |
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Country/Territory | United States |
City | Boston, MA |
Period | 6/07/08 → 11/07/08 |
Keywords
- DSPCA
- Hyperspectral data
- PCA
- Sparse representation
- SPCA