Spectral-spatial classification of hyperspectral imagery based on recurrent neural networks

Deep neural networks have recently been successfully explored to extract deep features for hyperspectral image classification. Recurrent neural networks (RNNs) are an important branch of the deep learning family, which are widely used for sequence analysis. Indeed, RNNs have been used to model the dependencies between the different spectral bands of hyperspectral image, inspired by the observation that hyperspectral pixels can be considered as spectral sequences. A disadvantage of such methods is that they don't consider the effect of neighborhood pixels on the final class label. In this letter, a RNN model is proposed for the spectral-spatial classification of hyperspectral image. Specifically, the hyperspectral image cube surrounding a central pixel is considered as a hyperspectral pixels sequence, and a RNN is used to model the dependencies between the different neighborhood pixels. The proposed RNN is conducted on two widely used hyperspectral image datasets. The experimental results demonstrate that the proposed approach provides a better performance than that of conventional methods.