A Modified Nonparametric Weight Feature Extraction Using Spatial and Spectral Information

Feature extraction is often applied for dimensionality reduction in hyperspectral data classification problems to mitigate the Hughes phenomenon. Some studies had proven that nonparametric weighted feature extraction (NWFE) is a powerful tool to extract well-described features for classification. NWFE concentrates only on the separability of spectral data, however, in many remotely sensed images, objects on the ground are much greater than one pixel. Hence, neighboring pixels are more likely to belong to the same class and form a homogeneous region. We present a scheme to fuse spatial information into NWFE, and from the real data experiments, we can find the proposed method outperforms the original NWFE.