Blind Source Separation in Polarimetric SAR Interferometry

International audiencePolarimetric incoherent target decomposition aims in access-ing physical parameters of illuminated scatters through the analysis of target coherence or covariance matrix. In this framework, Independent Component Analysis (ICA) was recently proposed as an alternative method to Eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high resolution SAR systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to classical Eigenvector decomposition and the inability to be employed in scenarios under Gaussian clutter assumption. First, a Monte Carlo approach is performed in order to investigate the bias in estimating the Touzi Target Scattering Vector Model (TSVM) parameters when ICA is employed. A RAMSES X-band image acquired over Brétigny, France is taken into consideration to investigate the bias estimation under different scenarios. Finally, some results in terms of POLinSAR coherence optimization [1] in the context of ICA are proposed

Polarimetric Incoherent Target Decomposition by Means of Independent Component Analysis

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

Evaluation of Multilook Effect in ICA Based ICTD for PolSAR Data Analysis

International audiencePolarimetric incoherent target decomposition aims in accessing physical parameters of illuminated scatters through the analysis of target coherence or covariance matrix. In this framework, Independent Component Analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high resolution SAR systems). In this paper a Monte Carlo approach is performed in order to investigate the bias in estimating Touzi's Target Scattering Vector Model parameters when ICA is employed. Simulated data and data from the P-band airborne dataset acquired by the Office National d'tudes et de Recherches Arospatiales (ON-ERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR are taken into consideration

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table

PSR J2030+3641: radio discovery and gamma-ray study of a middle-aged pulsar in the now identified Fermi-LAT source 1FGL J2030.0+3641

In a radio search with the Green Bank Telescope of three unidentified low Galactic latitude Fermi-LAT sources, we have discovered the middle-aged pulsar J2030+3641, associated with 1FGL J2030.0+3641 (2FGL J2030.0+3640). Following the detection of gamma-ray pulsations using a radio ephemeris, we have obtained a phase-coherent timing solution based on gamma-ray and radio pulse arrival times that spans the entire Fermi mission. With a rotation period of 0.2 s, spin-down luminosity of 3e34 erg/s, and characteristic age of 0.5 Myr, PSR J2030+3641 is a middle-aged neutron star with spin parameters similar to those of the exceedingly gamma-ray-bright and radio-undetected Geminga. Its gamma-ray flux is 1% that of Geminga, primarily because of its much larger distance, as suggested by the large integrated column density of free electrons, DM=246 pc/cc. We fit the gamma-ray light curve, along with limited radio polarimetric constraints, to four geometrical models of magnetospheric emission, and while none of the fits have high significance some are encouraging and suggest that further refinements of these models may be worthwhile. We argue that not many more non-millisecond radio pulsars may be detected along the Galactic plane that are responsible for LAT sources, but that modified methods to search for gamma-ray pulsations should be productive -- PSR J2030+3641 would have been found blindly in gamma rays if only >0.8 GeV photons had been considered, owing to its relatively flat spectrum and location in a region of high soft background.Comment: Accepted for publication in ApJ, 9 pages, 6 figure

Persistent scatterer aided facade lattice extraction in single airborne optical oblique images

We present a new method to extract patterns of regular facade structures from single optical oblique images. To overcome the missing three-dimensional information we incorporate structural information derived from Persistent Scatter (PS) point cloud data into our method. Single oblique images and PS point clouds have never been combined before and offer promising insights into the compatibility of remotely sensed data of different kinds. Even though the appearance of facades is significantly different, many characteristics of the prominent patterns can be seen in both types of data and can be transferred across the sensor domains. To justify the extraction based on regular facade patterns we show that regular facades appear rather often in typical airborne oblique imagery of urban scenes. The extraction of regular patterns is based on well established tools like cross correlation and is extended by incorporating a module for estimating a window lattice model using a genetic algorithm. Among others the results of our approach can be used to derive a deeper understanding of the emergence of Persistent Scatterers and their fusion with optical imagery. To demonstrate the applicability of the approach we present a concept for data fusion aiming at facade lattices extraction in PS and optical data

Applications of SAR Interferometry in Earth and Environmental Science Research

This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions