HBI-Net: Hierarchical Bitemporal Interaction Network for Directional Algal Bloom Change Detection in Multispectral Imagery

Satellite remote sensing constitutes a critical methodology for monitoring algal bloom dynamics in eutrophic lakes. However, the majority of existing research predominantly emphasizes single-date extraction or area-based statistics, thereby offering limited pixel-level characterization of bloom appearance and disappearance. These two phenomena bear distinct implications for water quality management: Bloom appearance indicates potential water-quality risks, whereas bloom disappearance often reflects the effectiveness of management interventions or natural decay processes. In this study, we conceptualize bloom monitoring as a directional change-detection problem and introduce the Algal Bloom Change Detection (ABCD) dataset, which comprises 5038 bitemporal Sentinel-2 multispectral image patches from Lake Taihu and Lake Chaohu, annotated into three categories: unchanged, bloom appearance, and bloom disappearance. Furthermore, we propose the Hierarchical Bitemporal Interaction Network (HBI-Net), which integrates a shared Swin-T backbone, a Hierarchical Bitemporal Interaction Block (HBI Block), and a Spatial-Channel Decoder (SC-Decoder). The HBI Block employs signed-temporal-difference gating to maintain change directionality across multiple scales, while the SC-Decoder performs spatial-channel recalibration prior to three-class classification. Experimental results on the ABCD dataset demonstrate that HBI-Net achieves a mean Intersection over Union (mIoU) of 88.08%, outperforming all compared methods; ablation studies further validate the contributions of both the HBI Block and the SC-Decoder. Additionally, when evaluated on the S2Looking building change-detection dataset, HBI-Net attains the highest mIoU, suggesting its potential applicability across different domains.