Deep Low-Rank Graph Convolutional Subspace Clustering for Hyperspectral Image

Deep subspace clustering (DSC) has achieved considerable success in the classification task of hyperspectral images (HSIs) without background (defined as noisy samples) compared with traditional subspace clustering methods. Unfortunately, directly applying DSC to classify land-cover on HSI datasets with background may suffer from the degradation of classification performance. In this article, we propose an effective deep low-rank graph convolutional subspace clustering (DLR-GCSC) framework for improving the performance of land-cover classification on HSI datasets with background. Specifically, we design a joint spatial-spectral network to extract band- and patch-level features simultaneously by combining 1-D and 2-D autoencoders. Moreover, we construct a low-rank constrained fully connected layer as a self-expression layer in the network to make the joint features more discriminative. To reduce the influence of noisy samples and obtain an informative affinity matrix, we recast the joint features into a non-Euclidean domain by introducing graph convolution. Finally, spectral clustering is applied to the informative affinity matrix to obtain the classification results. Experiments on three benchmark HSI datasets show that our proposed method achieves competitive classification performance to the state-of-the-art methods on both HSI data with background and without background.