Self-Supervised Graph Masked Autoencoders for Hyperspectral Image Classification

Traditional supervised deep learning (DL) methods for hyperspectral image (HSI) classification are severely limited by the quality and quantity of labels. Furthermore, existing feature extraction methods generally lack the fusion of multiscale feature information, struggling to handle complex scenarios. To counter these problems, this work investigates a feature extraction module based on self-supervised graph masked autoencoders (SGMAEs). It innovatively employs graph masked autoencoders to achieve self-supervised label-free feature extraction for the complete set of samples, utilizing a multiscale graph convolutional network encoder (MGCNE) and cross correlation decoder (CCD) to extract and fuse multiscale spatial-spectral features of HSI data, respectively. Specifically, the HSI data is first converted into an edge-masked perturbed graph to label-freely extract multiscale feature representations of all pixel samples, and then fed into the MGCNE to obtain multilayer feature vectors for the pixel nodes. To reconstruct the masked edges for the fusion of multiscale features, the CCD applies cross correlation calculations to the nodes of the true edges at the masked positions and the fake edges at the random positions. The contrastive learning loss function is proposed for training of the autoencoder, which calculates the loss for the existence estimates of edges generated by cross correlation calculations. The pretrained MGCNE possesses an efficient self-supervised multiscale spatial-spectral feature extraction capability, along with strong generalizability, which significantly improves the accuracy of various mainstream models in downstream classification tasks. Extensive experiments and analyses on multiple HSI datasets demonstrate that our proposed SGMAE significantly enhances the model performance of various supervised classifiers and achieves superior performance in comparison to mainstream models.