Feature selection for hyperspectral data based on recursive support vector machines
In this article, a feature selection algorithm for hyperspectral data based on a recursive support vector machine (R-SVM) is proposed. The new algorithm follows the scheme of a state-of-the-art feature selection algorithm, SVM recursive feature elimination or SVM-RFE, and uses a new ranking criterion derived from the R-SVM. Multiple SVMs are used to address the multiclass problem. The algorithm is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to select the most informative bands and the resulting subsets of the bands are compared with SVM-RFE using the accuracy of classification as the evaluation of the effectiveness of the feature selection. The experimental results for an agricultural case study indicate that the feature subset generated by the newly proposed algorithm is generally competitive with SVM-RFE in terms of classification accuracy and is more robust in the presence of noise.
Document Type: Research Article
Affiliations: Centre for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100101, P. R. China
Publication date: 01 January 2009
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content