Unsupervised Spectral-Spatial Feature Extraction With Generalized Autoencoder for Hyperspectral Imagery

In this letter, we discuss unsupervised feature extraction on hyperspectral imagery (HSI) and propose a novel approach based on autoencoder (AE) networks to extract spectral-spatial features from HSI. Our approach takes the data relations into consideration, i.e., the input dependency with adjacent inputs, which the normal AE-based feature extractors often disregard. Specifically, the loss function of the normal AE is modified so as to make pixels share the common features among the neighboring pixels. The process enables the generation of smooth compressed images represented by features provided by the AE. Numerical experiments were conducted on real-world HSI data sets for land cover classification. The results demonstrated that spectral-spatial features extracted by our approach are more discriminative for land cover classification than those done by conventional approaches.