This letter proposes a robust framework for polarimetric covariance symmetries classification in Synthetic Aperture Radar (SAR) images applying a pre-screening on the data looks before they are used to perform inferences. More specifically, the devised method improves the performance of a previous work based on the exploitation of the special structures assumed by the covariance/coherence matrix when symmetric scattering mechanisms dominate the polarimetric returns. To do this, the algorithm selects first the most homogeneous data through the cancellation of those sharing the highest Generalized Inner Product (GIP) values computed with the use of the geometric barycenters. Then, the procedure based on Model Order Selection (MOS) developed in the homogeneous case is applied on the filtered data. The conducted tests show the potentiality of the proposed method in correctly classifying the observed scene of L-band real-recorded SAR data with respect to its standard counterpart.

This letter exploits the intrinsic selectivity properties of the median to enhance the covariance symmetry classification in polarimetric synthetic aperture radar (PolSAR) images. More in detail, the median matrices are utilized to properly detect and remove outliers in the data belonging to a reference window, in turn used to estimate the covariance structure of the pixel under test. Hence, the scene is classified in terms of the structures assumed by the covariance under specific symmetric scattering mechanisms. To do this, for each pixel under test, the data in a reference window are filtered through the application of a generalized inner product (GIP)-based procedure involving the median matrix in its computation. The filtered data are then used as input to a model order selection (MOS)-based procedure for the final scene classification. Tests conducted on L-band real-recorded SAR data show the effectiveness of the devised framework.

In this paper we investigate the problem of detecting single coherent scatterers in multidimensional (elevation-velocity) SAR imaging. We exploit both phase and amplitude information and a GLRT approach. Multipass/multiview SAR data are used and performances are assessed referring to two different models for the scatterers: deterministic and Gaussian model.

In this paper, stochastic processes with higher order statistical functions decomposable into an almost-periodic function plus a residual term not containing finite-strength additive sinewave components are considered. These processes arise in mobile communications when almost-cyclostationary (ACS) processes pass through time-varying channels. They include as special case the generalized almost-cyclostationary processes which, in turn, include the ACS processes. In the paper, the problem of estimating the Fourier coefficients of the (generalized) Fourier series expansion of the almost-periodic component of higher order statistical functions is addressed. Estimators are proposed for cyclic temporal cross moment and cumulants. They are proved to be mean square consistent and asymptotically complex Normal under mild assumptions on the memory of the processes expressed in terms of summability of cross cumulants. Numerical results confirm the theoretical results and the derived rate of convergence to zero of bias and standard deviation of the estimators.

In this work we analyze the properties of integration time and azimuth coverage of bistatic SAR configurations. The kinematics of antennas is modelled as a motion at a constant velocity along a linear path. Orbits can be at different heights above the ground and have crossing projections on a horizontal plane. Footprints on the reference ground plane move at different velocities. Performances of bistatic SAR configurations are compared to those of monostatic ones.

Bistatic synthetic aperture radar (SAR) is an extension of traditional monostatic SAR, which increases the flexibility in designing SAR missions. We describe a scheme for the computation of integration time and azimuth coverage of bistatic SARs based on space-time diagrams. A classification of bistatic SAR configurations is introduced in terms of size and velocity on the ground of antenna footprints. Bistatic SAR regimes are also identified.

A successful mathematical description of natural landscapes relies upon a class of random processes known as fractional Brownian motions (fBms), which may exhibit correlation with long-range dependence (LRD). In remote sensing applications, the sensor observes a certain real sceneBand records dataIfor successive signal processing tasks. Assuming thatBis modeled as an fBm, does the recorded signalIpreserve the LRD character ofB? More in general, can we relate the Hurst coefficient (an index of LRD) of the real scene to that of the recorded data? We address the problem in a simplified setup in which the data are related to (the slope of) the original scene through a zero-memory mapping. A mathematical framework is presented in which the above questions can be answered in the asymptotic regime of infinite data size. The effect of the finite sample size is also investigated. The mathematical model is also validated by real data, which are collected by a synthetic aperture radar that is mounted onboard of ERS-1/2 satellites.

Coherence is the key factor in Synthetic Aperture Radar Interferometry. We study the baseline decorrelation due to antenna spatial diversity in order to take into account the effect of the surface statistic roughness in a more general case. As a model of surface roughness we use the fractional Brownian motion

A self-similar natural scene, characterized by a Hurst coefficient HX>1/2, is remotely sensed. The physical process of data acquisition is modeled as a non-linear zero-memory function of the natural scene slope. Do the data collected by the sensor preserve the long-range-dependence feature? If they do, what about the relationship between the Hurst coefficient of the illuminated scene and that of the collected data? This is our first investigation of the issue; as such, we resort to an extremely simplified setup allowing us to highlight some interesting problem features

The authors present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows the authors to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.