Hyperspectral Image Classification Based on Spectral Graph and Bidirectional LSTM Network

Abstract

Convolutional neural networks (CNNs) have achieved cracking performance in the hyperspectral image (HSI) classification task. Nevertheless, most of them cannot meet what we expect when the numbers of labeled samples are small. Also, due to the rectangular convolution kernels, the long-range context information within HSIs is cannot fully be explored. To solve these problems, we propose a semi-supervised method based on the graph convolutional network (GCN) and bidirectional Long Short-Term Memory (Bi-LSTM). First, HSIs are over segmented into various superpixels and GCN is employed for mining the advanced spectral features. Second, we input the obtained spectral features to the Bi-LSTM model for exploring global spatial features. Due to the diverse receptive fields, the short-and long-range spatial relations can be discovered simultaneously. Finally, we map the features from region-level to pixel-level for classifying HSIs. The positive experimental results counted on two HSIs demonstrate that our method is superior to some popular methods.