Neighborhood Feature Pooling for Remote Sensing Image Classification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908756567130112 |
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| author | Nia, Fahimeh Orvati Mohammadi, Amirmohammad Kharsa, Salim Al Naikare, Pragati Hampel-Arias, Zigfried Peeples, Joshua |
| author_facet | Nia, Fahimeh Orvati Mohammadi, Amirmohammad Kharsa, Salim Al Naikare, Pragati Hampel-Arias, Zigfried Peeples, Joshua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25077 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neighborhood Feature Pooling for Remote Sensing Image Classification Nia, Fahimeh Orvati Mohammadi, Amirmohammad Kharsa, Salim Al Naikare, Pragati Hampel-Arias, Zigfried Peeples, Joshua Computer Vision and Pattern Recognition Image and Video Processing 68T07 I.4.8; I.2.10 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. |
| title | Neighborhood Feature Pooling for Remote Sensing Image Classification |
| topic | Computer Vision and Pattern Recognition Image and Video Processing 68T07 I.4.8; I.2.10 |
| url | https://arxiv.org/abs/2510.25077 |