轻量级改进提升雷达影像土地利用分割精度，尤其改善稀有类别和水体检测
Enhanced LULC Segmentation via Lightweight Model Refinements on ALOS-2 SAR Data
- 引入高分辨率特征、渐进式上采样头和自适应权重因子三重轻量优化
- 在全日本雷达数据集上显著提升稀有类别的分割准确率，水体检测指标全面提高
- 无需增加复杂度，适合大范围遥感应用与资源受限场景
本研究基于ALOS-2单极化（HH）SAR数据，针对日本全国尺度的土地利用/土地覆盖（LULC）语义分割任务，以及配套的二值水体检测任务展开。在SAR-W-MixMAE自监督预训练基础上，针对合成孔径雷达密集预测中常见的边界过平滑、细长结构漏检、长尾标签下稀有类别退化等问题，提出三种轻量级改进：(i) 将高分辨率特征注入多尺度解码器；(ii) 设计渐进式精炼上采样头，交替进行卷积精炼与分步上采样；(iii) 在focal+dice损失中引入α-尺度因子调节类别重权。所提模型在全日本ALOS-2 LULC基准测试中表现一致提升，尤其对低频类别效果显著，并在标准评估指标上改善了水体检测性能。
原文摘要 · Abstract (English)
This work focuses on national-scale land-use/land-cover (LULC) semantic segmentation using ALOS-2 single-polarization (HH) SAR data over Japan, together with a companion binary water detection task. Building on SAR-W-MixMAE self-supervised pretraining [1], we address common SAR dense-prediction failure modes, boundary over-smoothing, missed thin/slender structures, and rare-class degradation under long-tailed labels, without increasing pipeline complexity. We introduce three lightweight refinements: (i) injecting high-resolution features into multi-scale decoding, (ii) a progressive refine-up head that alternates convolutional refinement and stepwise upsampling, and (iii) an $α$-scale factor that tempers class reweighting within a focal+dice objective. The resulting model yields consistent improvements on the Japan-wide ALOS-2 LULC benchmark, particularly for under-represented classes, and improves water detection across standard evaluation metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。