Holographic Subsurface Imaging with Variance Weighted Canopy-Kmeans Clutter Suppression Method

Holographic subsurface radar (HSR) is an active remote sensing technology, proven to be highly effective in detecting shallow buried objects. However, HSR is often subject to strong reflected clutter, noise, and other interference from the ground surface in practice, resulting in poor imaging results. To address these issues, this paper proposes a clutter suppression method combining the Canopy-Kmeans clustering algorithm and variance-weighted L1 norm minimization (VWLNM). This method first uses the Canopy-Kmeans clustering algorithm to divide the radar data into several clusters, then weights the clusters based on the prior that the variance of rows and columns in the target area is significantly greater than that of the non-target area. Finally, the weights of the clutter and noise clusters are attenuated by minimizing the L1 norm of the objective function. Experimental results show that the proposed method effectively restores shallow buried targets while eliminating clutter.