# Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection

arXiv: https://arxiv.org/abs/2609.32716 · HTML: https://mlsanity.com/paper/2609.32716/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 26 Sep 2026.

## Builds on (most cited first, 2 of 2)

- [COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/2304.00721/) (2023)
- [NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images](https://mlsanity.com/paper/2303.17448/) (2023)

## Abstract (arXiv)

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.