1. Hyperspectral Feature Extraction Using Total Variation Component Analysis.
- Author
-
Rasti, Behnood, Ulfarsson, Magnus Orn, and Sveinsson, Johannes R.
- Subjects
HYPERSPECTRAL imaging systems ,FEATURE extraction ,PRINCIPAL components analysis ,SIGNAL-to-noise ratio ,REMOTE sensing - Abstract
In this paper, a novel feature extraction method, called orthogonal total variation component analysis (OTVCA), is proposed for remotely sensed hyperspectral data. The features are extracted by minimizing a total variation (TV) penalized optimization problem. The TV penalty promotes piecewise smoothness of the extracted features which is useful for classification. A cyclic descent algorithm called OTVCA-CD is proposed for solving the minimization problem. In the experiments, OTVCA is applied on a rural hyperspectral image having low spatial resolution and an urban hyperspectral image having high spatial resolution. The features extracted by OTVCA show considerable improvements in terms of classification accuracy compared with features extracted by other state-of-the-art methods. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF