通过邻域特征聚合提升遥感图像分类的纹理表征能力
Neighborhood Feature Pooling for Remote Sensing Image Classification
- 设计邻域特征池化层，利用卷积操作捕捉局部特征间相似性模式
- 在多个数据集和模型上均显著提升分类性能，参数增加极少
- 适合关注遥感图像纹理建模与轻量级网络优化的研究者
本文提出一种新型池化层——邻域特征池化（Neighborhood Feature Pooling, NFP），旨在增强遥感图像分类中的纹理感知表征学习。NFP 通过在特征维度上聚合局部相似性模式，捕捉相邻空间特征之间的关系。该层仅使用标准卷积操作实现，可无缝集成到现有神经网络架构中，附加参数极少。在多个基准数据集和主干模型上的大量实验表明，相比传统池化策略，NFP 能持续提升分类性能，同时保持计算效率。结果验证了基于邻域的特征聚合在捕捉遥感影像中判别性纹理信息方面的有效性。
原文摘要 · Abstract (English)
In this work, we introduce Neighborhood Feature Pooling (NFP), a novel pooling layer designed to enhance texture-aware representation learning for remote sensing image classification. The proposed NFP layer captures relationships between neighboring spatial features by aggregating local similarity patterns across feature dimensions. Implemented using standard convolutional operations, NFP can be seamlessly integrated into existing neural network architectures with minimal additional parameters. Extensive experiments across multiple benchmark datasets and backbone models demonstrate that NFP consistently improves classification performance compared to conventional pooling strategies, while maintaining computational efficiency. These results highlight the effectiveness of neighborhood-based feature aggregation for capturing discriminative texture information in remote sensing imagery.
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