Robust Principal Component Analysis Techniques for Ground Scene Estimation in SAR Imagery

Robust principal component analysis (RPCA) has been widely used for processing and interpreting high-dimensional data in different applications such as data classification, face recognition, video analytics, and recommendation system design. However, the advancement of multisensor-based technologies and the emergence of large datasets have highlighted the limitations of traditional matrix-based models, which have paved the way for higher-order extensions such as tensor RPCA (TRPCA) techniques. These techniques can be useful for ground scene estimation (GSE) in synthetic aperture radar (SAR) imagery. GSE estimates the clutter-plus-noise content in the scene, and therefore, change detection (CD) methods can benefit, reducing the number of false alarms. This article presents two new GSE methods for SAR imagery based on robust PCA techniques. The first proposed method uses the RPCA via principal component pursuit (PCP) to obtain the GSE-RPCA. The second method uses TRPCA via new tensor nuclear norm (TNN) to obtain the GSE-TRPCA. The methodology allows the GSE to be obtained through a generalized regularization parameter. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve both optimization problems. Experimental results are evaluated considering real SAR imagery from datasets acquired with the CARABAS II and ALOS PALSAR systems, respectively. Additionally, the proposed techniques were evaluated under several input characteristics, e.g., eight-image stacks and image pairs. Both GSE techniques are more robust to outliers and missing data when compared to existing solutions found in the literature. Finally, GSE-TRPCA achieved a minimum-square error performance of 0.0018 for some of the evaluated scenarios.

Automated summary

Robust principal component analysis (RPCA) and tensor RPCA (TRPCA) techniques are valuable for ground scene estimation (GSE) in synthetic aperture radar (SAR) imagery, improving the performance of change detection methods.