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Robust Principal Component Analysis Techniques for Ground Scene Estimation in SAR Imagery
Aeronautics Institute of Technology, Brazil.
Aeronautics Institute of Technology, Brazil.
University of Campinas, Brazil.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312X
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2023 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 16, p. 9697-9710Article in journal (Refereed) Published
Abstract [en]

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 data sets 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 signal separation 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 (FA). This paper 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 two data sets 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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023. Vol. 16, p. 9697-9710
Keywords [en]
ground scene estimation, Ground support, Principal component analysis, Radar polarimetry, Remote sensing, Robust principal component analysis, SAR imagery, Sparse matrices, Synthetic aperture radar, Tensor robust PCA, Tensors, Clustering algorithms, Face recognition, Image analysis, Matrix algebra, Radar imaging, Analysis techniques, Principal-component analysis, Remote-sensing, Robust PCA, Synthetic Aperture Radar Imagery
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-25552DOI: 10.1109/JSTARS.2023.3324732ISI: 001093375500005Scopus ID: 2-s2.0-85174844045OAI: oai:DiVA.org:bth-25552DiVA, id: diva2:1810191
Available from: 2023-11-07 Created: 2023-11-07 Last updated: 2023-11-21Bibliographically approved

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Pettersson, Mats

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