Graph-based semi-supervised Weighted band selection for classification of hyperspectral data
When the number of labeled samples is limited, traditional supervised feature selection techniques often fail due to unrepresentative sample problem. However, in classification of hyperspectral data, the labeled samples are often difficult, expensive or timeconsuming to obtain. Recently, several semi-supervised feature selection algorithms have been proposed, which aim at doing feature selection using some unlabeled data. In this paper, a novel semi-supervised band selection method which aims to improve classification accuracy of highspectral remote sensing data is proposed. This algorithm combines Fisher’s criteria and Graph Laplacian, exploits labeled and unlabeled samples at the same time. With the help of the generalized eigenvalue method, we can easily get the loading factors from the linear transformation matrix to determine the weight value for each band. Experimental results demonstrate effectiveness of the proposed method.
Ling Chen Rui Huang Wei Huang
School of Communication and Information Engineering, Shanghai University, PR China
国际会议
上海
英文
1123-1126
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）