In this work, we propose neighborhood feature pooling (NFP) as a novel texture feature extraction method for remote sensing image classification. The NFP layer captures relationships between neighboring inputs and efficiently aggregates local similarities across feature dimensions. Implemented using convolutional layers, NFP can be seamlessly integrated into any network. Results comparing the baseline models and the NFP method indicate that NFP consistently improves performance across diverse datasets and architectures while maintaining minimal parameter overhead.

翻译：本文提出了一种用于遥感影像分类的新型纹理特征提取方法——邻域特征池化。NFP层通过捕捉相邻输入之间的关联性，在特征维度上高效聚合局部相似性。该方法基于卷积层实现，可无缝集成至任意网络架构。与基线模型的对比实验表明，NFP方法在不同数据集和网络结构下均能持续提升分类性能，同时保持极低的参数量开销。