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Change detection (CD) in heterogeneous remote sensing images is a practicaland challenging issue for real-life emergencies. In the past decade, theheterogeneous CD problem has significantly benefited from the development ofdeep neural networks (DNN). However, the data-driven DNNs always perform like ablack box where the lack of interpretability limits the trustworthiness andcontrollability of DNNs in most practical CD applications. As a strongknowledge-driven tool to measure correlation between random variables, Copulatheory has been introduced into CD, yet it suffers from non-robust CDperformance without manual prior selection for Copula functions. To address theabove issues, we propose a knowledge-data-driven heterogeneous CD method(NN-Copula-CD) based on the Copula-guided interpretable neural network. In ourNN-Copula-CD, the mathematical characteristics of Copula are designed as thelosses to supervise a simple fully connected neural network to learn thecorrelation between bi-temporal image patches, and then the changed regions areidentified via binary classification for the correlation coefficients of allimage patch pairs of the bi-temporal images. We conduct in-depth experiments onthree datasets with multimodal images (e.g., Optical, SAR, and NIR), where thequantitative results and visualized analysis demonstrate both the effectivenessand interpretability of the proposed NN-Copula-CD.

NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images