Change detection in UWB SAR images based on robust principal component analysis

• This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method. © 2020 by the authors.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Blind source separation

CARABAS-II

Change detection

RPCA

Synthetic aperture radar

Data handling

Economic and social effects

Errors

Image analysis

Multiobjective optimization

Radar imaging

Ultra-wideband (UWB)

Likelihood ratio tests

Multi-objective optimization problem

Number of false alarms

Probability of detection

Receiver operating characteristic curves

Regularization parameters

Robust principal component analysis

Synthetic aperture radar (SAR) images

Principal component analysis

Publication and Content Type

ref (subject category)

art (subject category)