Polarimetric Incoherent Target Decomposition by Means of Independent Component Analysis

Abstract

International audienceThis paper presents an alternative approach for polarimetric incoherent target decomposition dedicated to the analysis of very-high resolution POLSAR images. Given the non-Gaussian nature of the heterogeneous POLSAR clutter due to the increase of spatial resolution, the conventional methods based on the eigenvector target decomposition can ensure uncorrelation of the derived backscattering components at most. By introducing the Independent Component Analysis (ICA) in lieu of the eigenvector decomposition, our method is rather deriving statistically independent components. The adopted algorithm - FastICA, uses the non-Gaussianity of the components as the criterion for their independence. Considering the eigenvector decomposition as being analogues to the Principal Component Analysis (PCA), we propose the generalization of the ICTD methods to the level of the Blind Source Separation (BSS) techniques (comprising both PCA and ICA). The proposed method preserves the invariance properties of the conventional ones, appearing to be robust both with respect to the rotation around the line of sight and to the change of the polarization basis. The efficiency of the method is demonstrated comparatively, using POLSAR Ramses X-band and ALOS L-band data sets. The main differences with respect to the conventional methods are mostly found in the behaviour of the second most dominant component, which is not necessarily orthogonal to the first one. The potential of retrieving non-orthogonal mechanisms is moreover demonstrated using synthetic data. On expense of a negligible entropy increase, the proposed method is capable of retrieving the edge diffraction of an elementary trihedral by recognizing dipole as the second component

Information extraction by blind source separation from polarimetric SAR data

“…The ICA is a blind source separation technique based on higher order statistical moments and cumulants whose utility has already been explored in many different research areas, such as wireless communications, data compression and brain imaging applications. The results presented in [7] proved it to be a very promising area in polarimetry, mainly when nonGaussian heterogeneous clutters (inherent to high resolution SAR systems) are under study. The theoretical potential in estimating similar entropy and first component, when compared to traditional eigenvector decomposition, but rather a second most dominant component independent with respect to the first one and unconstrained by the orthogonality introduces an alternative way of physically interpreting a polarimetric SAR image.…”

“…In [7], a new strategy to polarimetric target decomposition was presented by incorporating the independent component analysis (ICA) as an alternative to identify the canonical scattering mechanisms within an image cell. The ICA is a blind source separation technique based on higher order statistical moments and cumulants whose utility has already been explored in many different research areas, such as wireless communications, data compression and brain imaging applications.…”

“…Most methods described in the literature focus on the Hermitian, semidefinite positive coherence or covariance matrix [4]. Nevertheless, the investigation of higher order moments has recently sparked great interest of the SAR community, introducing supplementary information to the clutter analysis and consequently leading to new ICTD approaches [5], [6], [7].…”

Blind image separation using pyramid technique

“…Then, two-dimensional (2-D) astrophysical image components were achieved by segmenting separated 1-D original signals and rearranging these segments as columns or rows. Recently, researchers implemented sparse component analysis (SCA) to improve the method of blind image separation [19,20]. These approaches could accurately separate the image mixtures using linear clustering when the linear clustering has less run time than super-plane clustering techniques, and the image sources are sparse enough [21].…”

Unraveling hydrometeor mixtures in polarimetric radar measurements

“…Aside from uncorrelated components, in Fig. 14, we also show the results of employing the introduced FastICA method (with kurtosis value for the measure of non-Gaussianity), following its demonstrated benefits in the framework of the SAR decomposition theory (Besic et al, 2015;Pralon et al, 2016). The principal advantage of this tool with respect to PCA would be the lack of the orthogonality constraint.…”