AI 中文总结
本文提出区域-局部Copula证据融合方法，通过局部依赖异常与门控机制改善异构遥感变化检测的混合区域定位，在Lake和UK数据集上提升kappa系数并验证了场景依赖的校准与门控效益。
AI 中文摘要
超像素Copula模型为异构遥感变化检测提供了稳定的区域证据，但每个区域仅分配单一标签，限制了在混合超像素内的定位能力。本文提出了一种区域-局部Copula证据融合方法，在保留区域决策结构的同时引入空间变化的局部依赖异常。独立拟合的局部模型刻画了与未变化跨图像关系的偏离。参考排序和上尾门控将这些异常转换，以便与连续的区域置信度进行融合。我们推导了由此产生的区域依赖局部决策阈值，并确定了门控等效于重新参数化未门控融合的条件。在Lake和UK数据集上，整图优化配置分别达到0.78136和0.90817的kappa系数，并改善了混合区域和边界决策。对十个训练子集进行的四折回顾性空间验证证实了互补的局部信息，未门控参考融合使平均kappa分别提高了0.00693和0.01793。固定门控在UK上获得了更大的提升0.03353，但在Lake上仅为0.00041。这些结果支持区域-局部依赖交互作用，同时表明校准和门控具有场景依赖的益处。
英文摘要
Superpixel copula models provide stable regional evidence for heterogeneous remote sensing change detection, but a single label per region limits localization within mixed superpixels. This letter develops a region-local copula evidence fusion method that retains the regional decision structure while introducing spatially varying local dependence anomalies. Independently fitted local models characterize departures from unchanged cross-image relationships. Reference ranking and an upper-tail gate transform these anomalies for fusion with continuous regional confidence. We derive the resulting regiondependent local decision threshold and identify a condition under which gating is equivalent to reparameterizing ungated fusion. On Lake and UK, whole-image optimized configurations achieve kappa coefficients of 0.78136 and 0.90817 and improve mixedregion and boundary decisions. Four-fold retrospective spatial validation over ten training subsets confirms complementary local information, with ungated reference fusion increasing mean kappa by 0.00693 and 0.01793. Fixed gating yields a larger UK gain of 0.03353 but only 0.00041 on Lake. These results support regional-local dependence interaction, while showing that calibration and gating have scene-dependent benefits.
