Hierarchical Contrastive Learning for Multigranularity Ship Classification With Learnable Class Queries

Ship targets in remote sensing images can be categorized at various granularities due to variations in image quality, ranging from general ship categories to fine-grained classes like Nimitz (NT)-class carriers. Traditional studies mainly focus on fine-grained ship classification (FGSC), often neglecting samples observed at coarser-grained levels. Samples distributed across multiple granularity levels exhibit semantic relationships among their annotated classes, enabling hierarchical knowledge transfer during model training. This article incorporates two semantic relationships into deep learning-based representation learning and class prediction: parent-child relationships across levels and mutual exclusivity among sibling categories. For hierarchical representation learning, the proposed hierarchical contrastive learning algorithm extracts category-specific representations from input images and aligns them with their semantic relationships, ensuring that parent and child categories share similarities while sibling categories remain distinct. For hierarchical class predictions, a novel consistency loss (CL) ensures coherence in probability distributions between parent and child categories. Specifically, cross-entropy (CE) loss is employed to impose mutual exclusivity among sibling categories. In this article, a multimodal dataset is also designedly developed for hierarchical classification, which integrates optical and synthetic aperture radar (SAR) images across multiple hierarchical levels. Experiments on two popular datasets and a multimodal dataset demonstrate that the proposed method outperforms state-of-the-art approaches in hierarchical multigranularity ship classification.