Enhancing subsurface exploration: A comprehensive review of advanced clutter removal techniques for ground penetrating radar imaging

Ground penetrating radar (GPR) has emerged as a powerful non-destructive imaging tool and widely used technology for subsurface imaging across various fields. However, one of the significant challenges in GPR data analysis is clutter, which arises from various sources such as surface roughness, antenna ringing, and electromagnetic interference. This clutter obscures the desired subsurface information and hinders interpretation. In recent years, significant progress has been made in developing clutter removal techniques to enhance the quality and reliability of GPR data. This comprehensive review paper presents an in-depth examination of clutter removal techniques for GPR images, categorizing them into traditional signal processing methods, dictionary learning techniques, low-rank and sparse-based algorithms, and deep learning-based approaches. By discussing the principles, advantages, and limitations of each technique, along with their applications and recent advancements, this review aims to provide insights into the state-of-the-art in clutter removal for GPR imagery, addressing key challenges and paving the way for future research directions in this critical domain.