Unsupervised False-Alarm-Controllable Change Detection in Heterogeneous Remote Sensing Images Based on Copula Theory

Change detection (CD) in heterogeneous remote sensing images plays a crucial role in earth observation tasks, such as disaster monitoring and destruction assessment. Recent advancements in heterogeneous CD studies have substantially enhanced the capability to detect changes, but existing methodologies frequently lack effective control mechanisms for increasing false alarms when facing different heterogeneous scenes. Consequently, even with a high detection rate for changes, the real changes co-exist with lots of false alarms, thereby reducing the reliability and practical utility of the CD results. To address this issue, inspired by the insight of adaptive thresholding for false alarm control in constant false alarm rate (CFAR) detection, we propose a copula theory-based CD framework, named FAR-Aware-Copula-CD, to control false alarm rate (FAR) in heterogeneous CD. In the proposed FAR-Aware-Copula-CD, the heterogeneous CD problem is represented as a binary hypothesis testing problem. Then, the binary hypothesis testing problem is solved by a generalized likelihood ratio test based on copula theory, which effectively characterizes change statistics based on superpixel-level dependence within various heterogeneous image pairs. Finally, the decision thresholds of the copula-based change statistics are determined so as to satisfy the FAR constraint and ensure that the final CD result approaches a prespecified false alarm rate. Our FAR-Aware-Copula-CD provides a new approach for implementing controllable false alarms in heterogeneous CD tasks. Experimental results on four real-world datasets demonstrate the effectiveness of our proposed method.