Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle

Speckle phenomenon in synthetic aperture radar (SAR) images makes their visual and automatic interpretation a difficult task. To reduce strong fluctuations due to speckle, total variation (TV) regularization has been proposed by several authors to smooth out noise without blurring edges. A specificity of SAR images is the presence of strong scatterers having a radiometry several orders of magnitude larger than their surrounding region. These scatterers, especially present in urban areas, limit the effectiveness of TV regularization as they break the assumption of an image made of regions of constant radiometry. To overcome this limitation, we propose in this paper an image decomposition approach. There exist numerous methods to decompose an image into several components, notably to separate textural and geometrical information. These decomposition models are generally recast as energy minimization problems involving a different penalty term for each of the components. In this framework, we propose an energy suitable for the decomposition of SAR images into speckle, a smooth background, and strong scatterers, and discuss its minimization using max-flow/min-cut algorithms. We make the connection between the minimization problem considered, involving the L0 pseudonorm, and the generalized likelihood ratio test used in detection theory. The proposed decomposition jointly performs the detection of strong scatterers and the estimation of the background radiometry. Given the increasing availability of time series of SAR images, we consider the decomposition of a whole time series. New change detection methods can be based on the temporal analysis of the components obtained from our decomposition.