# Neighborhood Feature Pooling for Remote Sensing Image Classification

Source: [Neighborhood Feature Pooling for Remote Sensing Image Classification](<https://www.sota2.com/research/paper/6902f0db3ef7ccd74e5e4052>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-14T05:00:04.658Z

Authors: Fahimeh Orvati Nia, Amirmohammad Mohammadi, Salim Al Kharsa, Pragati Naikare, Zigfried Hampel-Arias, Joshua Peeples

Published: 2025-10-29T01:24:49.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.25077>)

[PDF](<https://arxiv.org/pdf/2510.25077.pdf>)