Sparse Representations for Hyperspectral Data Classification
Document Type
Conference Proceeding
Publication Date
12-1-2008
Journal / Book Title
IEEE
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.
DOI
10.1109/IGARSS.2008.4779058
Montclair State University Digital Commons Citation
Siddiqui, Salman; Robila, Stefan; Peng, Jing; and Wang, Dajin, "Sparse Representations for Hyperspectral Data Classification" (2008). Department of Computer Science Faculty Scholarship and Creative Works. 550.
https://digitalcommons.montclair.edu/compusci-facpubs/550
Published Citation
Siddiqui, S., Robila, S., Peng, J., & Wang, D. (2008, July). Sparse representations for hyperspectral data classification. In IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium (Vol. 2, pp. II-577). IEEE.