用概率分布建模类别，提升少样本灾害图像识别准确率。
Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net
- 将类别原型视为概率分布，通过巴氏距离比较进行分类
- 在5类1样本设置下达80.7%准确率，灾害数据集最高78.3%
- 适合遥感图像中数据稀缺的灾害快速识别场景
自然灾害与人为灾害频发，亟需在标注数据有限条件下具备鲁棒性的视觉识别系统。然而，灾害图像分类受限于数据稀疏、类内差异大及遥感影像特有的领域复杂性。为此，我们提出注意力巴氏距离特征聚合网络（ABHFA-Net），一种新型少样本学习框架：将类别原型建模为概率分布，通过巴氏距离进行分类；引入空间通道注意力机制增强特征判别力，并设计基于巴氏距离的对比软最大值损失以提升类别可分性。在基准数据集（CIFAR-FS、FC-100、miniImageNet、tieredImageNet）和真实灾害数据集（AIDER、CDD、MEDIC）上的大量实验表明，该方法有效。尤其在CIFAR-FS上，5类1样本与5样本设置下分别达到80.7%和92.3%准确率，优于现有最先进方法；在灾害数据集上，模型持续提升性能，于AIDER上实现1样本68.2%、5样本78.3%准确率，凸显其在真实场景中的鲁棒性。该工作为数据稀缺且时效性强的遥感灾害图像识别提供了可靠解决方案。
原文摘要 · Abstract (English)
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.
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