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.

翻译：本研究聚焦于利用ALOS-2单极化（HH）SAR数据对日本进行全国尺度的土地利用/土地覆盖（LULC）语义分割，并附带一项二元水体检测任务。基于SAR-W-MixMAE自监督预训练[1]，我们解决了SAR密集预测中的常见失效模式——边界过度平滑、细长结构漏检以及长尾标签下稀有类别性能退化——且未增加流程复杂度。我们引入了三项轻量化改进：（i）将高分辨率特征注入多尺度解码过程；（ii）采用交替进行卷积优化与逐步上采样的渐进式优化上采样头；（iii）在focal+dice损失函数中引入调节类别重新加权的$α$尺度因子。所得模型在日本全境ALOS-2 LULC基准测试中取得了持续改进，尤其在代表性不足的类别上表现突出，并在各项标准评估指标上提升了水体检测性能。