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：本研究提出邻域特征池化（NFP）作为一种新颖的遥感影像分类纹理特征提取方法。NFP层通过捕获相邻输入之间的关联关系，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。基线模型与NFP方法的对比实验结果表明：NFP在不同数据集和网络架构中均能持续提升分类性能，同时保持极低的参数量开销。