Abstract
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.
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Cite this article
APA 7
Ji, Z., Yin, J., & Yang, J. (2026). Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection. https://omanscience.com/en/articles/region-local-copula-evidence-fusion-for-heterogeneous-remote-sensing-change-detection
MLA 9
Ji, Zhiyuan, et al. "Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection." https://omanscience.com/en/articles/region-local-copula-evidence-fusion-for-heterogeneous-remote-sensing-change-detection.
Chicago (author–date)
Ji, Zhiyuan, Junjun Yin, and Jian Yang. 2026. "Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection." https://omanscience.com/en/articles/region-local-copula-evidence-fusion-for-heterogeneous-remote-sensing-change-detection.
Harvard
Ji, Z., Yin, J. and Yang, J. (2026) 'Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection', Available at: https://omanscience.com/en/articles/region-local-copula-evidence-fusion-for-heterogeneous-remote-sensing-change-detection.
Vancouver
Ji Z, Yin J, Yang J. Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection. https://omanscience.com/en/articles/region-local-copula-evidence-fusion-for-heterogeneous-remote-sensing-change-detection
IEEE
Z. Ji, J. Yin, and J. Yang, "Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection," https://omanscience.com/en/articles/region-local-copula-evidence-fusion-for-heterogeneous-remote-sensing-change-detection.