Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net

The rising incidence of natural and human-induced disasters necessitates robust visual recognition systems capable of operating under limited labeled data conditions. However, disaster-related image classification remains challenging due to data scarcity, high intra-class variability, and domain-specific complexities in remote sensing imagery. To address these challenges, we propose the Attention Bhattacharyya Distance-based Feature Aggregation Network (ABHFA-Net), a novel few-shot learning (FSL) framework that models class prototypes as probability distributions and performs classification via Bhattacharyya distance-based comparison. Our approach integrates a spatial channel attention mechanism to enhance discrimiantive feature learning in the few-shot context and introduces a Bhattacharyya-based contrastive softmax loss for improved class separability. Extensive experiments on both benchmark datasets (CIFAR-FS, FC-100, miniImageNet, tieredImageNet) and real-world disaster datasets (AIDER, CDD, MEDIC) demonstrate the effectiveness of the proposed method. In particular, ABHFA-Net achieves 80.7% and 92.3% accuracy on CIFAR-FS under 5-way 1-shot and 5-shot settings, respectively, outperforming existing state-of-the-art methods. On disaster datasets, the model consistently improves classification performance, achieving up to 68.2% (1-shot) and 78.3% (5-shot) accuracy on AIDER, highlighting its robustness in real-world scenarios. These results establish ABHFA-Net as a strong and practical solution for few-shot disaster image classification, particularly in data-scarce and time-critical remote sensing applications. The code repository for our work is available at https://github.com/GreedYLearner1146/ABHFA-Net.