Learning Spatial–Spectral Features for Hyperspectral Image Classification

Combining spatial information with spectral information for classifying hyperspectral images can dramatically improve the performance. This paper proposes a simple but innovative framework to automatically generate spatial–spectral features for hyperspectral image classification. Two unsupervised learning methods— $K$ -means and principal component analysis—are utilized to learn the spatial feature bases in each decorrelated spectral band. The spatial feature representations are extracted with these spatial feature bases. Then, spatial–spectral features are generated by concatenating the spatial feature representations in all/principal spectral bands. The experimental results indicate that the proposed method is flexible enough to generate rich spatial–spectral features and can outperform the other state-of-the-art methods.