Neighborhood Feature Pooling for Remote Sensing Image Classification

TL;DR

This paper introduces Neighborhood Feature Pooling (NFP), a new pooling layer that improves texture-aware remote sensing image classification by capturing local feature relationships, leading to better performance with minimal added complexity.

Contribution

The paper proposes NFP, a novel pooling method that enhances texture representation in remote sensing images and integrates seamlessly into existing neural networks.

Findings

NFP improves classification accuracy across multiple datasets.

NFP maintains computational efficiency.

NFP outperforms traditional pooling methods.

Abstract

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.