Robust SAR Change Detection Using Hierarchical Clustering With Adaptive Parameter Tuning

Change detection in synthetic aperture radar imagery is challenging due to noise, complex nonlinear features, and variations in underlying data distributions. Traditional clustering-based methods often make strong assumptions about data structure, struggle with noise, and fail to handle the intricate characteristics of radar images effectively. Furthermore, deriving change maps directly from the difference image can introduce confusion and increase detection errors. This article presents a novel change detection approach that employs the hierarchical density-based clustering algorithm to improve the accuracy and robustness of change map generation. The method automatically identifies clusters of varying densities while filtering out noise, ensuring a more precise change detection process. A new hyperparameter optimization strategy is introduced to enhance clustering performance by tuning key parameters based on the silhouette index. In addition, an adaptive thresholding mechanism leverages cluster probability and statistical measures, including location and dispersion, to refine the selection of meaningful changes. The methodology consists of computing a difference image using a logarithmic ratio operator, optimizing clustering parameters, computing high-change probability clusters, and generating a refined change map. Experimental results on multiple datasets demonstrate the effectiveness of the proposed approach in adapting to different data complexities, improving detection accuracy, and minimizing false positives compared to existing methods.