A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis

Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images.

[1]  Viet Thuy Vu,et al.  Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection , 2019, IEEE Geoscience and Remote Sensing Letters.

[2]  Viet Thuy Vu,et al.  Wavelength-Resolution SAR Incoherent Change Detection Based on Image Stack , 2017, IEEE Geoscience and Remote Sensing Letters.

[3]  Mats I. Pettersson,et al.  A Statistical Analysis for Wavelength-Resolution SAR Image Stacks , 2020, IEEE Geoscience and Remote Sensing Letters.

[4]  Mats I. Pettersson,et al.  The Stability of UWB Low-Frequency SAR Images , 2016, IEEE Geoscience and Remote Sensing Letters.

[5]  Mats I. Pettersson,et al.  False Alarm Reduction in Wavelength-Resolution SAR Change Detection Using Adaptive Noise Canceler , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Christofer Schwartz,et al.  A UEP Method for Imaging Low-Orbit Satellites Based on CCSDS Recommendations , 2018, IEEE Geoscience and Remote Sensing Letters.

[7]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[8]  W. Pierson,et al.  Change detection for low-frequency SAR ground surveillance , 2005 .

[9]  Liliana Borcea,et al.  Synthetic Aperture Radar Imaging and Motion Estimation via Robust Principal Component Analysis , 2013, SIAM J. Imaging Sci..

[10]  Mats I. Pettersson,et al.  Change Detection in UWB SAR Images Based on Robust Principal Component Analysis , 2020, Remote. Sens..

[11]  Lars M. H. Ulander,et al.  A challenge problem for detection of targets in foliage , 2006, SPIE Defense + Commercial Sensing.

[12]  Scott T. Acton,et al.  Speckle reducing anisotropic diffusion , 2002, IEEE Trans. Image Process..

[13]  Jun Li,et al.  A Novel Moving Target Detection Method Based on RPCA for SAR Systems , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Mats I. Pettersson,et al.  Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack , 2020, Sensors.

[15]  Thierry Bouwmans,et al.  Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance , 2014, Comput. Vis. Image Underst..