Clustering Hypergraph U-Net With High-Order Topology-Aware Pooling and Unpooling for Hyperspectral Image Classification

Recently, hypergraph convolutional networks (HGCNs) have shown considerable potential in hyperspectral image (HSI) classification for their superiority in capturing the complex many-to-many structure among multiple ground object samples. However, the absence of high-order pooling operation for many-to-many structure restricts networks to shallow architectures, limiting the extraction of deep discriminative features. To break this limitation, we propose a clustering hypergraph U-Net (CHGUnet) incorporating high-order topology-aware pooling and unpooling operations. Specifically, a sparse fuzzy clustering (SFC) strategy is first designed to explore multivariate relationships among multiple superpixels of ground objects, thereby building the high-order topological association hypergraph for hierarchical hypergraph convolution in the U-Net architecture. Meanwhile, the hypergraph pooling (HGPool) and hypergraph unpooling (HGUnPool), designed with high-order topology-aware assessments on both views of node and hyperedge, are adapted for adjusting high-order topological association to learn more suitable structured features that are beneficial to classification. Finally, a lightweight depth separable convolutional branch is integrated into the network through a cross-level interactive fusion (CLIF) mechanism, enhancing the superpixel-level features derived from hypergraph convolution with complementary pixel-level details. Such a CHGUnet can learn comprehensive discriminative features from a high-order topological perspective, and the experiments on three HSI datasets have proven its competitive performance in contrast to other advanced methods.