Feature Extraction of Hyperspectral Images Based on Subspace Band Selection and Transform-Domain Recursive Filtering

During the feature extraction of hyperspectral images, a single filter cannot acquire complete information. To solve the problem, this paper proposes a feature extraction method based on subspace band selection and transform-domain recursive filtering. The proposed method contains three steps: Firstly, the target hyperspectral image is divided into multiple subsets of adjacent bands. Secondly, the Lasso-based band selection approach is adopted to compute the sparsity coefficient of each band. The bands in each subset are then ranked by the coefficient. Based on the ranking, the band with the highest coefficient is extracted from each subset, and used to reconstruct the hyperspectral data. Finally, the reconstructed hyperspectral image is processed through transform-domain recursive filtering, producing the features to be classified. Taking the support vector machine (SVM) as the classifier, our method was tested on several real hyperspectral image datasets. The results show that our method has a better classification accuracy than the other band selection methods.