LightFormer: A lightweight and efficient decoder for remote sensing image segmentation

• URL: http://arxiv.org/abs/2504.10834v1
• Date: Tue, 15 Apr 2025 03:25:39 GMT
• Title: LightFormer: A lightweight and efficient decoder for remote sensing image segmentation
• Authors: Sihang Chen, Lijun Yun, Ze Liu, JianFeng Zhu, Jie Chen, Hui Wang, Yueping Nie,
• Abstract summary: We introduce LightFormer, a lightweight decoder for time-critical tasks that involve unstructured targets.<n>LightFormer employs a feature-fusion and refinement module built on channel processing and a learnable gating mechanism to aggregate multi-scale, multi-range information efficiently.<n>On the ISPRS Vaihingen benchmark, LightFormer attains 99.9% of GLFFNet's mIoU while requiring only 14.7% of its FLOPs and 15.9% of its parameters.
• Score: 12.003743832147403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning techniques have achieved remarkable success in the semantic segmentation of remote sensing images and in land-use change detection. Nevertheless, their real-time deployment on edge platforms remains constrained by decoder complexity. Herein, we introduce LightFormer, a lightweight decoder for time-critical tasks that involve unstructured targets, such as disaster assessment, unmanned aerial vehicle search-and-rescue, and cultural heritage monitoring. LightFormer employs a feature-fusion and refinement module built on channel processing and a learnable gating mechanism to aggregate multi-scale, multi-range information efficiently, which drastically curtails model complexity. Furthermore, we propose a spatial information selection module (SISM) that integrates long-range attention with a detail preservation branch to capture spatial dependencies across multiple scales, thereby substantially improving the recognition of unstructured targets in complex scenes. On the ISPRS Vaihingen benchmark, LightFormer attains 99.9% of GLFFNet's mIoU (83.9% vs. 84.0%) while requiring only 14.7% of its FLOPs and 15.9% of its parameters, thus achieving an excellent accuracy-efficiency trade-off. Consistent results on LoveDA, ISPRS Potsdam, RescueNet, and FloodNet further demonstrate its robustness and superior perception of unstructured objects. These findings highlight LightFormer as a practical solution for remote sensing applications where both computational economy and high-precision segmentation are imperative.

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