Classification of Hyperspectral Image Based on Shadow Enhancement by Dynamic Stochastic Resonance

Information extraction of shadow areas in hyperspectral images (HSIs) has always been a difficult problem in HSI processing. Dynamic stochastic resonance (DSR) theory has proved that the noise contained in the signal can enhance the strength of the original signal and improve the signal-to-noise ratio (SNR). And it has been applied in signal and image processing,communication and other fields. In this paper, DSR theory is introduced to the shadow enhancement in HSIs for the first time. The spatial and spectral dimensions of the shadow areas in a HSI could be enhanced by the DSR respectively. Then, the enhanced shadow should be fused with the other areas in the HSI. Finally, the fused image could be classified to explore the information in the HSI. The experimental result show that the DSR has promising prospect in the shadow enhancement in HSIs, and can help to improve the classification.