FCFDA: Fine–Coarse–Fine Progressive Graph Framework With Distribution Alignment for Hyperspectral Image Change Detection

Graph convolutional networks (GCNs) have attracted significant attention in hyperspectral image (HSI) change detection (CD) due to their capability to perform shape-adaptive convolutions and capture complex patterns within HSIs. Existing GCN-based methods typically preprocess bitemporal HSIs into graphs using a specific superpixel segmentation. However, this preprocessing step limits the modeling of spatial topologies to a fixed scale. Besides, these methods do not consider distribution shifts between bitemporal HSIs. To overcome these limitations, this article proposes a fine–coarse–fine progressive graph framework with distribution alignment (FCFDA) to learn progressive features across multilevel graphs for HSI-CD. Specifically, for each bitemporal HSI, we generate multiple hierarchical segmentations ranging from fine to coarse by gradually merging neighboring superpixels and subsequently transforming these segmentations into multilevel graphs. Second, instead of simply concatenating features from different hierarchies, FCFDA integrates them progressively from fine to coarse and then back to fine, generating subtle features tailored to the pixel-wise CD task. Finally, an effective distribution alignment (DA) method is designed to align the feature space of the bitemporal HSIs, thus mitigating the adverse effects of distribution shifts. Experiments conducted on real HSI-CD datasets demonstrate the effectiveness and superiority of the FCFDA.