Abstract
Few-shot learning in remote sensing remains challenging due to three factors: the scarcity of labeled data, substantial domain shifts, and the multi-scale nature of geospatial objects. To address these issues, we introduce Adaptive Multi-Scale Correlation Meta-Network (AMC-MetaNet), a lightweight yet powerful framework with three key innovations: (i) correlation-guided feature pyramids for capturing scale-invariant patterns, (ii) an adaptive channel correlation module (ACCM) for learning dynamic cross-scale relationships, and (iii) correlation-guided meta-learning that leverages correlation patterns instead of conventional prototype averaging. Unlike prior approaches that rely on heavy pre-trained models or transformers, AMC-MetaNet is trained from scratch with only $\sim600K$ parameters, offering $20\times$ fewer parameters than ResNet-18 while maintaining high efficiency ($<50$ms per image inference). AMC-MetaNet achieves up to 86.65\% accuracy in 5-way 5-shot classification on various remote sensing datasets, including EuroSAT, NWPU-RESISC45, UC Merced Land Use, and AID. Our results establish AMC-MetaNet as a computationally efficient, scale-aware framework for real-world few-shot remote sensing.
Abstract (translated)
在遥感领域中，少量样本学习（few-shot learning）仍面临三大挑战：标记数据的稀缺性、显著的域偏移以及地理空间对象的多尺度特性。为了解决这些问题，我们引入了自适应多尺度相关元网络（Adaptive Multi-Scale Correlation Meta-Network, AMC-MetaNet），这是一种轻量级但功能强大的框架，具有三项关键创新： 1. 相关引导特征金字塔：用于捕获尺度不变的模式。 2. 自适应通道相关模块（ACCM）：用于学习动态跨尺度关系。 3. 基于相关性的元学习：利用相关性模式而非传统的原型平均。 与以往依赖重型预训练模型或变换器的方法不同，AMC-MetaNet从头开始训练，仅使用约60万个参数。这使得它比ResNet-18少20倍的参数量，同时保持了高效性（每张图像推理时间低于50毫秒）。在包括EuroSAT、NWPU-RESISC45、UC Merced Land Use和AID在内的多个遥感数据集上，AMC-MetaNet实现了高达86.65%的精度，在五类五样本分类任务中表现优异。我们的研究结果将AMC-MetaNet确立为一种计算效率高且尺度感知度强的框架，适用于现实世界的少量样本遥感应用。
URL
https://arxiv.org/abs/2601.12308
