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Change Detection in Wavelength-Resolution SAR Image Stack Based on Tensor Robust PCA
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0001-9267-6718
Aeronautics Institute of Technology, Brazil.
State University of Campinas, Brazil.
Aeronautics Institute of Technology, Brazil.
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2024 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 21, article id 4014505Article in journal (Refereed) Published
Abstract [en]

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2. Authors

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 21, article id 4014505
Keywords [en]
CARABAS-II, change detection, Convex functions, Electron tubes, Principal component analysis, Radar polarimetry, SAR, Surveillance, Synthetic aperture radar, tensor robust PCA, Tensors, Errors, Image analysis, Radar imaging, Tracking radar, CARABAS, Principal-component analysis, Robust PCA, Wavelength resolution
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-26791DOI: 10.1109/LGRS.2024.3431683ISI: 001301004100001Scopus ID: 2-s2.0-85199549288OAI: oai:DiVA.org:bth-26791DiVA, id: diva2:1887846
Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2024-10-21Bibliographically approved

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Ramos, Lucas P.Pettersson, MatsVu, Viet Thuy

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