有限目标分辨率监督下的高效连续DEM重建
Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision
- Northwestern Polytechnical University（西北工业大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
SCOPE通过可重用系数场在低分辨率监督下实现高分辨率DEM重建，在未见尺度上误差降低约12%，计算开销极小。
中文摘要 AI 辅助
高分辨率数字高程模型（DEM）支持地球观测应用，但成对的训练参考通常仅在较粗的输出分辨率下可用。因此，重建更精细的地形网格需要超越监督尺度的有效迁移以及对密集查询计算的控制。为解决这一问题，SCOPE从较粗分辨率的配对中学习连续地形表示。它在低分辨率网格上预测潜在系数场，并通过基函数评估和几何引导的集成融合重用局部傅里叶残差函数。这将高维系数预测与输出网格构建分离。在地理分布的海陆样本上的实验评估了监督重建、未见尺度推断、跨域泛化和理论计算。在主要监督尺度评估中，SCOPE在六个指标上领先于对比方法。在训练因子三倍的未见因子下，陆地重建相对于双三次插值将RMSE和MAE降低了约12%，误差接近目标尺度微调。九倍输出密度增加仅使乘加运算增加约2%。在保留的外部海洋区域上的冻结模型验证中，在自降采样下相对于DEM特定隐式基线EBCF-CDEM将RMSE降低约19%，在跨产品输入下降低约2%，同时在两种设置下均获得低于LIIF-MS的RMSE。这些结果证明了可重用系数场在监督分辨率之外以低增量算术成本进行精确重建的价值。
英文摘要
High-resolution digital elevation models (DEMs) support Earth observation applications, but paired training references are often available only at coarser output resolutions. Reconstructing finer terrain grids therefore requires both effective transfer beyond the supervised scale and control of dense-query computation. To address this problem, SCOPE learns a continuous terrain representation from coarser-resolution pairs. It predicts a latent coefficient field on the low-resolution grid and reuses local Fourier residual functions through basis evaluation and geometry-guided ensemble fusion. This separates high-dimensional coefficient prediction from output-grid construction. Experiments on geographically distributed land--ocean samples assess supervised reconstruction, unseen-scale inference, cross-domain generalization, and theoretical computation. SCOPE leads the compared methods across six metrics in the main supervised-scale evaluation. At an unseen factor three times the training factor, land reconstruction reduces RMSE and MAE by approximately 12\% relative to bicubic interpolation, with errors close to target-scale fine-tuning. Ninefold output density increases counted multiply--accumulate operations by only about 2\%. Frozen-model validation on held-out external marine regions reduces RMSE relative to the DEM-specific implicit baseline EBCF-CDEM by approximately 19\% under self-downsampling and 2\% with cross-product inputs, while also yielding lower RMSE than LIIF-MS in both settings. These results demonstrate the value of reusable coefficient fields for accurate reconstruction beyond the supervised resolution with low incremental arithmetic cost.
