MFFP-Net: Building Segmentation in Remote Sensing Images via Multi-Scale Feature Fusion and Foreground Perception Enhancement
The accurate segmentation of small target buildings in high-resolution remote sensing images remains challenging due to two critical issues: (1) small target buildings often occupy few pixels in complex backgrounds, leading to frequent background confusion, and (2) significant intra-class variance c...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/11/1875 |
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| Summary: | The accurate segmentation of small target buildings in high-resolution remote sensing images remains challenging due to two critical issues: (1) small target buildings often occupy few pixels in complex backgrounds, leading to frequent background confusion, and (2) significant intra-class variance complicates feature representation compared to conventional semantic segmentation tasks. To address these challenges, we propose a novel Multi-Scale Feature Fusion and Foreground Perception Enhancement Network (MFFP-Net). This framework introduces three key innovations: (1) a Multi-Scale Feature Fusion (MFF) module that hierarchically aggregates shallow features through cross-level connections to enhance fine-grained detail preservation, (2) a Foreground Perception Enhancement (FPE) module that establishes pixel-wise affinity relationships within foreground regions to mitigate intra-class variance effects, and (3) a Dual-Path Attention (DPA) mechanism combining parallel global and local attention pathways to jointly capture structural details and long-range contextual dependencies. Experimental results demonstrate that the IoU of the proposed method achieves improvements of 0.44%, 0.98% and 0.61% compared to mainstream state-of-the-art methods on the WHU Building, Massachusetts Building, and Inria Aerial Image Labeling datasets, respectively, validating its effectiveness in handling small targets and intra-class variance while maintaining robustness in complex scenarios. |
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| ISSN: | 2072-4292 |